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  • 1.
    Dahlbom, Anders
    et al.
    Högskolan i Skövde.
    Maria, Riveiro
    Högskolan i Skövde.
    König, Rikard
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    Brattberg, Peter
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    Supporting Golf Coaching with 3D Modeling of Swings2014In: Sportinformatik X: Jahrestagung der dvs-Sektion Sportinformatik, Hamburg: Feldhaus Verlag GmbH & Co. KG , 2014, 10, p. 142-148Chapter in book (Refereed)
  • 2.
    Gabrielsson, Patrick
    et al.
    University of Borås, School of Business and IT.
    Johansson, Ulf
    University of Borås, School of Business and IT.
    König, Rikard
    University of Borås, School of Business and IT.
    Co-Evolving Online High-Frequency Trading Strategies Using Grammatical Evolution2014Conference paper (Refereed)
    Abstract [en]

    Numerous sophisticated algorithms exist for discovering reoccurring patterns in financial time series. However, the most accurate techniques available produce opaque models, from which it is impossible to discern the rationale behind trading decisions. It is therefore desirable to sacrifice some degree of accuracy for transparency. One fairly recent evolutionary computational technology that creates transparent models, using a user-specified grammar, is grammatical evolution (GE). In this paper, we explore the possibility of evolving transparent entry- and exit trading strategies for the E-mini S&P 500 index futures market in a high-frequency trading environment using grammatical evolution. We compare the performance of models incorporating risk into their calculations with models that do not. Our empirical results suggest that profitable, risk-averse, transparent trading strategies for the E-mini S&P 500 can be obtained using grammatical evolution together with technical indicators.

  • 3.
    Gabrielsson, Patrick
    et al.
    University of Borås, School of Business and IT.
    König, Rikard
    University of Borås, School of Business and IT.
    Johansson, Ulf
    University of Borås, School of Business and IT.
    Evolving Hierarchical Temporal Memory-Based Trading Models2013Conference paper (Refereed)
    Abstract [en]

    We explore the possibility of using the genetic algorithm to optimize trading models based on the Hierarchical Temporal Memory (HTM) machine learning technology. Technical indicators, derived from intraday tick data for the E-mini S&P 500 futures market (ES), were used as feature vectors to the HTM models. All models were configured as binary classifiers, using a simple buy-and-hold trading strategy, and followed a supervised training scheme. The data set was partitioned into multiple folds to enable a modified cross validation scheme. Artificial Neural Networks (ANNs) were used to benchmark HTM performance. The results show that the genetic algorithm succeeded in finding predictive models with good performance and generalization ability. The HTM models outperformed the neural network models on the chosen data set and both technologies yielded profitable results with above average accuracy.

  • 4.
    Gabrielsson, Patrick
    et al.
    University of Borås, School of Business and IT.
    König, Rikard
    University of Borås, School of Business and IT.
    Johansson, Ulf
    University of Borås, School of Business and IT.
    Hierarchical Temporal Memory-based algorithmic trading of financial markets2012Conference paper (Refereed)
    Abstract [en]

    This paper explores the possibility of using the Hierarchical Temporal Memory (HTM) machine learning technology to create a profitable software agent for trading financial markets. Technical indicators, derived from intraday tick data for the E-mini S&P 500 futures market (ES), were used as features vectors to the HTM models. All models were configured as binary classifiers, using a simple buy-and-hold trading strategy, and followed a supervised training scheme. The data set was divided into a training set, a validation set and three test sets; bearish, bullish and horizontal. The best performing model on the validation set was tested on the three test sets. Artificial Neural Networks (ANNs) were subjected to the same data sets in order to benchmark HTM performance. The results suggest that the HTM technology can be used together with a feature vector of technical indicators to create a profitable trading algorithm for financial markets. Results also suggest that HTM performance is, at the very least, comparable to commonly applied neural network models.

  • 5.
    Johansson, Ulf
    et al.
    University of Borås, School of Business and IT.
    Boström, Henrik
    König, Rikard
    University of Borås, School of Business and IT.
    Extending Nearest Neighbor Classification with Spheres of Confidence2008Conference paper (Refereed)
  • 6.
    Johansson, Ulf
    et al.
    University of Borås, School of Business and IT.
    König, Rikard
    University of Borås, School of Business and IT.
    Linusson, Henrik
    University of Borås, School of Business and IT.
    Löfström, Tuve
    University of Borås, School of Business and IT.
    Boström, Henrik
    University of Borås, School of Business and IT.
    Rule Extraction with Guaranteed Fidelity2014Conference paper (Refereed)
    Abstract [en]

    This paper extends the conformal prediction framework to rule extraction, making it possible to extract interpretable models from opaque models in a setting where either the infidelity or the error rate is bounded by a predefined significance level. Experimental results on 27 publicly available data sets show that all three setups evaluated produced valid and rather efficient conformal predictors. The implication is that augmenting rule extraction with conformal prediction allows extraction of models where test set errors or test sets infidelities are guaranteed to be lower than a chosen acceptable level. Clearly this is beneficial for both typical rule extraction scenarios, i.e., either when the purpose is to explain an existing opaque model, or when it is to build a predictive model that must be interpretable.

  • 7.
    Johansson, Ulf
    et al.
    University of Borås, School of Business and IT.
    König, Rikard
    University of Borås, School of Business and IT.
    Löfström, Tuve
    University of Borås, School of Business and IT.
    Niklasson, Lars
    University of Borås, School of Business and IT.
    Increasing Rule Extraction Accuracy by Post-processing GP Trees2008In: Proceedings of the Congress on Evolutionary Computation, IEEE Press , 2008, p. 3010-3015Conference paper (Refereed)
    Abstract [en]

    Genetic programming (GP), is a very general and efficient technique, often capable of outperforming more specialized techniques on a variety of tasks. In this paper, we suggest a straightforward novel algorithm for post-processing of GP classification trees. The algorithm iteratively, one node at a time, searches for possible modifications that would result in higher accuracy. More specifically, the algorithm for each split evaluates every possible constant value and chooses the best. With this design, the post-processing algorithm can only increase training accuracy, never decrease it. In this study, we apply the suggested algorithm to GP trees, extracted from neural network ensembles. Experimentation, using 22 UCI datasets, shows that the post-processing results in higher test set accuracies on a large majority of datasets. As a matter of fact, for two setups of three evaluated, the increase in accuracy is statistically significant.

  • 8.
    Johansson, Ulf
    et al.
    University of Borås, School of Business and IT.
    König, Rikard
    University of Borås, School of Business and IT.
    Löfström, Tuve
    University of Borås, School of Business and IT.
    Niklasson, Lars
    Using Imaginary Ensembles to Select GP Classifiers2010In: Genetic Programming: 13th European Conference, EuroGP 2010, Istanbul, Turkey, April 7-9, 2010, Proceedings / [ed] A.I. et al. Esparcia-Alcazar, Springer-Verlag Berlin Heidelberg , 2010, p. 278-288Conference paper (Refereed)
    Abstract [en]

    When predictive modeling requires comprehensible models, most data miners will use specialized techniques producing rule sets or decision trees. This study, however, shows that genetically evolved decision trees may very well outperform the more specialized techniques. The proposed approach evolves a number of decision trees and then uses one of several suggested selection strategies to pick one specific tree from that pool. The inherent inconsistency of evolution makes it possible to evolve each tree using all data, and still obtain somewhat different models. The main idea is to use these quite accurate and slightly diverse trees to form an imaginary ensemble, which is then used as a guide when selecting one specific tree. Simply put, the tree classifying the largest number of instances identically to the ensemble is chosen. In the experimentation, using 25 UCI data sets, two selection strategies obtained significantly higher accuracy than the standard rule inducer J48.

  • 9.
    Johansson, Ulf
    et al.
    University of Borås, School of Business and IT.
    König, Rikard
    University of Borås, School of Business and IT.
    Löfström, Tuve
    University of Borås, School of Business and IT.
    Sönströd, Cecilia
    University of Borås, School of Business and IT.
    Niklasson, Lars
    Post-processing Evolved Decision Trees2009In: Foundations of Computational Intelligence / [ed] Ajith Abraham, Springer Verlag , 2009, p. 149-164Chapter in book (Other academic)
    Abstract [en]

    Although Genetic Programming (GP) is a very general technique, it is also quite powerful. As a matter of fact, GP has often been shown to outperform more specialized techniques on a variety of tasks. In data mining, GP has successfully been applied to most major tasks; e.g. classification, regression and clustering. In this chapter, we introduce, describe and evaluate a straightforward novel algorithm for post-processing genetically evolved decision trees. The algorithm works by iteratively, one node at a time, search for possible modifications that will result in higher accuracy. More specifically, the algorithm, for each interior test, evaluates every possible split for the current attribute and chooses the best. With this design, the post-processing algorithm can only increase training accuracy, never decrease it. In the experiments, the suggested algorithm is applied to GP decision trees, either induced directly from datasets, or extracted from neural network ensembles. The experimentation, using 22 UCI datasets, shows that the suggested post-processing technique results in higher test set accuracies on a large majority of the datasets. As a matter of fact, the increase in test accuracy is statistically significant for one of the four evaluated setups, and substantial on two out of the other three.

  • 10.
    Johansson, Ulf
    et al.
    University of Borås, School of Business and IT.
    König, Rikard
    University of Borås, School of Business and IT.
    Löfström, Tuwe
    University of Borås, School of Business and IT.
    Boström, Henrik
    University of Borås, School of Business and IT.
    Evolved Decision Trees as Conformal Predictors2013Conference paper (Refereed)
    Abstract [en]

    In conformal prediction, predictive models output sets of predictions with a bound on the error rate. In classification, this translates to that the probability of excluding the correct class is lower than a predefined significance level, in the long run. Since the error rate is guaranteed, the most important criterion for conformal predictors is efficiency. Efficient conformal predictors minimize the number of elements in the output prediction sets, thus producing more informative predictions. This paper presents one of the first comprehensive studies where evolutionary algorithms are used to build conformal predictors. More specifically, decision trees evolved using genetic programming are evaluated as conformal predictors. In the experiments, the evolved trees are compared to decision trees induced using standard machine learning techniques on 33 publicly available benchmark data sets, with regard to predictive performance and efficiency. The results show that the evolved trees are generally more accurate, and the corresponding conformal predictors more efficient, than their induced counterparts. One important result is that the probability estimates of decision trees when used as conformal predictors should be smoothed, here using the Laplace correction. Finally, using the more discriminating Brier score instead of accuracy as the optimization criterion produced the most efficient conformal predictions.

  • 11.
    Johansson, Ulf
    et al.
    University of Borås, School of Business and IT.
    König, Rikard
    University of Borås, School of Business and IT.
    Niklasson, Lars
    Evolving a Locally Optimized Instance Based Learner2008In: Proceeding of The 2008 International Conference on Data Mining, CSREA Press , 2008, p. 124-129Conference paper (Refereed)
  • 12.
    Johansson, Ulf
    et al.
    University of Borås, School of Business and IT.
    König, Rikard
    University of Borås, School of Business and IT.
    Niklasson, Lars
    Genetic Rule Extraction Optimizing Brier Score2010In: Genetic and Evolutionary Computation Conference, GECCO 2010, Proceedings of the 12th annual conference on Genetic and evolutionary computation / [ed] Martin Pelikan, Jürgen Branke, ACM , 2010, p. 1007-1014Conference paper (Refereed)
    Abstract [en]

    Most highly accurate predictive modeling techniques produce opaque models. When comprehensible models are required, rule extraction is sometimes used to generate a transparent model, based on the opaque. Naturally, the extracted model should be as similar as possible to the opaque. This criterion, called fidelity, is therefore a key part of the optimization function in most rule extracting algorithms. To the best of our knowledge, all existing rule extraction algorithms targeting fidelity use 0/1 fidelity, i.e., maximize the number of identical classifications. In this paper, we suggest and evaluate a rule extraction algorithm utilizing a more informed fidelity criterion. More specifically, the novel algorithm, which is based on genetic programming, minimizes the difference in probability estimates between the extracted and the opaque models, by using the generalized Brier score as fitness function. Experimental results from 26 UCI data sets show that the suggested algorithm obtained considerably higher accuracy and significantly better AUC than both the exact same rule extraction algorithm maximizing 0/1 fidelity, and the standard tree inducer J48. Somewhat surprisingly, rule extraction using the more informed fidelity metric normally resulted in less complex models, making sure that the improved predictive performance was not achieved on the expense of comprehensibility.

  • 13.
    Johansson, Ulf
    et al.
    University of Borås, School of Business and IT.
    König, Rikard
    University of Borås, School of Business and IT.
    Niklasson, Lars
    Genetically Evolved Nearest Neighbor Ensembles2009In: Data Mining: Special Issue in Annals of Information Systems / [ed] Robert Stahlbock, Stefan Lessmann, Sven F. Crone, Springer Verlag , 2009, p. 299-313Chapter in book (Refereed)
    Abstract [en]

    Both theory and a wealth of empirical studies have established that ensembles are more accurate than single predictive models. For the ensemble approach to work, base classifiers must not only be accurate but also diverse, i.e., they should commit their errors on different instances. Instance based learners are, however, very robust with respect to variations of a dataset, so standard resampling methods will normally produce only limited diversity. Because of this, instance based learners are rarely used as base classifiers in ensembles. In this paper, we introduce a method where Genetic Programming is used to generate kNN base classifiers with optimized k-values and feature weights. Due to the inherent inconsistency in Genetic Programming (i.e. different runs using identical data and parameters will still produce different solutions) a group of independently evolved base classifiers tend to be not only accurate but also diverse. In the experimentation, using 30 datasets from the UCI repository, two slightly different versions of kNN ensembles are shown to significantly outperform both the corresponding base classifiers and standard kNN with optimized k-values, with respect to accuracy and AUC.

  • 14.
    Johansson, Ulf
    et al.
    University of Borås, School of Business and IT.
    König, Rikard
    University of Borås, School of Business and IT.
    Niklasson, Lars
    Inconsistency: Friend or Foe2007In: The International Joint Conference on Neural Networks, IEEE Press , 2007, p. 1383-1388Chapter in book (Other academic)
    Abstract [en]

    One way of obtaining accurate yet comprehensible models is to extract rules from opaque predictive models. When evaluating rule extraction algorithms, one frequently used criterion is consistency; i.e. the algorithm must produce similar rules every time it is applied to the same problem. Rule extraction algorithms based on evolutionary algorithms are, however, inherently inconsistent, something that is regarded as their main drawback. In this paper, we argue that consistency is an overvalued criterion, and that inconsistency can even be beneficial in some situations. The study contains two experiments, both using publicly available data sets, where rules are extracted from neural network ensembles. In the first experiment, it is shown that it is normally possible to extract several different rule sets from an opaque model, all having high and similar accuracy. The implication is that consistency in that perspective is useless; why should one specific rule set be considered superior? Clearly, it should instead be regarded as an advantage to obtain several accurate and comprehensible descriptions of the relationship. In the second experiment, rule extraction is used for probability estimation. More specifically, an ensemble of extracted trees is used in order to obtain probability estimates. Here, it is exactly the inconsistency of the rule extraction algorithm that makes the suggested approach possible.

  • 15.
    Johansson, Ulf
    et al.
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    Sundström, Malin
    University of Borås, Faculty of Textiles, Engineering and Business.
    Håkan, Sundell
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    Rickard, König
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    Jenny, Balkow
    University of Borås, Faculty of Textiles, Engineering and Business.
    Dataanalys för ökad kundförståelse2016Report (Other (popular science, discussion, etc.))
  • 16.
    Johansson, Ulf
    et al.
    University of Borås, School of Business and IT.
    Sönströd, Cecilia
    University of Borås, School of Business and IT.
    König, Rikard
    University of Borås, School of Business and IT.
    Accurate and Interpretable Regression Trees using Oracle Coaching2014Conference paper (Refereed)
    Abstract [en]

    In many real-world scenarios, predictive models need to be interpretable, thus ruling out many machine learning techniques known to produce very accurate models, e.g., neural networks, support vector machines and all ensemble schemes. Most often, tree models or rule sets are used instead, typically resulting in significantly lower predictive performance. The over- all purpose of oracle coaching is to reduce this accuracy vs. comprehensibility trade-off by producing interpretable models optimized for the specific production set at hand. The method requires production set inputs to be present when generating the predictive model, a demand fulfilled in most, but not all, predic- tive modeling scenarios. In oracle coaching, a highly accurate, but opaque, model is first induced from the training data. This model (“the oracle”) is then used to label both the training instances and the production instances. Finally, interpretable models are trained using different combinations of the resulting data sets. In this paper, the oracle coaching produces regression trees, using neural networks and random forests as oracles. The experiments, using 32 publicly available data sets, show that the oracle coaching leads to significantly improved predictive performance, compared to standard induction. In addition, it is also shown that a highly accurate opaque model can be successfully used as a pre- processing step to reduce the noise typically present in data, even in situations where production inputs are not available. In fact, just augmenting or replacing training data with another copy of the training set, but with the predictions from the opaque model as targets, produced significantly more accurate and/or more compact regression trees.

  • 17.
    Johansson, Ulf
    et al.
    University of Borås, School of Business and IT.
    Sönströd, Cecilia
    University of Borås, School of Business and IT.
    Löfström, Tuve
    University of Borås, School of Business and IT.
    König, Rikard
    University of Borås, School of Business and IT.
    Using Genetic Programming to Obtain Implicit Diversity2009Conference paper (Refereed)
    Abstract [en]

    When performing predictive data mining, the use of ensembles is known to increase prediction accuracy, compared to single models. To obtain this higher accuracy, ensembles should be built from base classifiers that are both accurate and diverse. The question of how to balance these two properties in order to maximize ensemble accuracy is, however, far from solved and many different techniques for obtaining ensemble diversity exist. One such technique is bagging, where implicit diversity is introduced by training base classifiers on different subsets of available data instances, thus resulting in less accurate, but diverse base classifiers. In this paper, genetic programming is used as an alternative method to obtain implicit diversity in ensembles by evolving accurate, but different base classifiers in the form of decision trees, thus exploiting the inherent inconsistency of genetic programming. The experiments show that the GP approach outperforms standard bagging of decision trees, obtaining significantly higher ensemble accuracy over 25 UCI datasets. This superior performance stems from base classifiers having both higher average accuracy and more diversity. Implicitly introducing diversity using GP thus works very well, since evolved base classifiers tend to be highly accurate and diverse.

  • 18.
    König, Rikard
    University of Borås, School of Business and IT.
    Enhancing genetic programming for predictive modeling2014Doctoral thesis, monograph (Other academic)
  • 19.
    König, Rikard
    University of Borås, School of Business and IT.
    Predictive Techniques and Methods for Decision Support in Situations with Poor Data Quality2009Licentiate thesis, monograph (Other academic)
    Abstract [en]

    Today, decision support systems based on predictive modeling are becoming more common, since organizations often collect more data than decision makers can handle manually. Predictive models are used to find potentially valuable patterns in the data, or to predict the outcome of some event. There are numerous predictive techniques, ranging from simple techniques such as linear regression,to complex powerful ones like artificial neural networks. Complex models usually obtain better predictive performance, but are opaque and thus cannot be used to explain predictions or discovered patterns. The design choice of which predictive technique to use becomes even harder since no technique outperforms all others over a large set of problems. It is even difficult to find the best parameter values for a specific technique, since these settings also are problem dependent. One way to simplify this vital decision is to combine several models, possibly created with different settings and techniques, into an ensemble. Ensembles are known to be more robust and powerful than individual models, and ensemble diversity can be used to estimate the uncertainty associated with each prediction. In real-world data mining projects, data is often imprecise, contain uncertainties or is missing important values, making it impossible to create models with sufficient performance for fully automated systems. In these cases, predictions need to be manually analyzed and adjusted. Here, opaque models like ensembles have a disadvantage, since the analysis requires understandable models. To overcome this deficiency of opaque models, researchers have developed rule extraction techniques that try to extract comprehensible rules from opaque models, while retaining sufficient accuracy. This thesis suggests a straightforward but comprehensive method for predictive modeling in situations with poor data quality. First, ensembles are used for the actual modeling, since they are powerful, robust and require few design choices. Next, ensemble uncertainty estimations pinpoint predictions that need special attention from a decision maker. Finally, rule extraction is performed to support the analysis of uncertain predictions. Using this method, ensembles can be used for predictive modeling, in spite of their opacity and sometimes insufficient global performance, while the involvement of a decision maker is minimized. The main contributions of this thesis are three novel techniques that enhance the performance of the purposed method. The first technique deals with ensemble uncertainty estimation and is based on a successful approach often used in weather forecasting. The other two are improvements of a rule extraction technique, resulting in increased comprehensibility and more accurate uncertainty estimations.

  • 20.
    König, Rikard
    et al.
    University of Borås, School of Business and IT.
    Johansson, Ulf
    University of Borås, School of Business and IT.
    Rule Extraction using Genetic Programming for Accurate Sales Forecasting2014Conference paper (Refereed)
    Abstract [en]

    The purpose of this paper is to propose and evaluate a method for reducing the inherent tendency of genetic programming to overfit small and noisy data sets. In addition, the use of different optimization criteria for symbolic regression is demonstrated. The key idea is to reduce the risk of overfitting noise in the training data by introducing an intermediate predictive model in the process. More specifically, instead of directly evolving a genetic regression model based on labeled training data, the first step is to generate a highly accurate ensemble model. Since ensembles are very robust, the resulting predictions will contain less noise than the original data set. In the second step, an interpretable model is evolved, using the ensemble predictions, instead of the true labels, as the target variable. Experiments on 175 sales forecasting data sets, from one of Sweden’s largest wholesale companies, show that the proposed technique obtained significantly better predictive performance, compared to both straightforward use of genetic programming and the standard M5P technique. Naturally, the level of improvement depends critically on the performance of the intermediate ensemble.

  • 21.
    König, Rikard
    et al.
    University of Borås, School of Business and IT.
    Johansson, Ulf
    University of Borås, School of Business and IT.
    Löfström, Tuve
    University of Borås, School of Business and IT.
    Niklasson, Lars
    Improving GP Classification Performance by Injection of Decision Trees2010Conference paper (Refereed)
    Abstract [en]

    This paper presents a novel hybrid method combining genetic programming and decision tree learning. The method starts by estimating a benchmark level of reasonable accuracy, based on decision tree performance on bootstrap samples of the training set. Next, a normal GP evolution is started with the aim of producing an accurate GP. At even intervals, the best GP in the population is evaluated against the accuracy benchmark. If the GP has higher accuracy than the benchmark, the evolution continues normally until the maximum number of generations is reached. If the accuracy is lower than the benchmark, two things happen. First, the fitness function is modified to allow larger GPs, able to represent more complex models. Secondly, a decision tree with increased size and trained on a bootstrap of the training data is injected into the population. The experiments show that the hybrid solution of injecting decision trees into a GP population gives synergetic effects producing results that are better than using either technique separately. The results, from 18 UCI data sets, show that the proposed method clearly outperforms normal GP, and is significantly better than the standard decision tree algorithm.

  • 22.
    König, Rikard
    et al.
    University of Borås, School of Business and IT.
    Johansson, Ulf
    University of Borås, School of Business and IT.
    Niklasson, Lars
    Finding the Tree in the Forest2010In: Proceeding of IADIS International Conference Applied Computing 2010 / [ed] Hans Weghorn, Pedro Isaías, Radu Vasio, IADIS Press , 2010, p. 135-142Conference paper (Refereed)
    Abstract [en]

    Decision trees are often used for decision support since they are fast to train, easy to understand and deterministic; i.e., always create identical trees from the same training data. This property is, however, only inherent in the actual decision tree algorithm, nondeterministic techniques such as genetic programming could very well produce different trees with similar accuracy and complexity for each execution. Clearly, if more than one solution exists, it would be misleading to present a single tree to a decision maker. On the other hand, too many alternatives could not be handled manually, and would only lead to confusion. Hence, we argue for a method aimed at generating a suitable number of alternative decision trees with comparable accuracy and complexity. When too many alternative trees exist, they are grouped and representative accurate solutions are selected from each group. Using domain knowledge, a decision maker could then select a single best tree and, if required, be presented with a small set of similar solutions, in order to further improve his decisions. In this paper, a method for generating alternative decision trees is suggested and evaluated. All in all,four different techniques for selecting accurate representative trees from groups of similar solutions are presented. Experiments on 19 UCI data sets show that it often exist dozens of alternative trees, and that one of the evaluated techniques clearly outperforms all others for selecting accurate and representative models.

  • 23.
    König, Rikard
    et al.
    University of Borås, School of Business and IT.
    Johansson, Ulf
    University of Borås, School of Business and IT.
    Niklasson, Lars
    Genetic Programming: a Tool for Flexible Rule Extraction2007In: IEEE Congress on Evolutionary Computation, IEEE Press , 2007, p. 1304-1310Chapter in book (Other academic)
  • 24.
    König, Rikard
    et al.
    University of Borås, School of Business and IT.
    Johansson, Ulf
    University of Borås, School of Business and IT.
    Niklasson, Lars
    G-REX: A Versatile Framework for Evolutionary Data Mining2008Conference paper (Refereed)
  • 25.
    König, Rikard
    et al.
    University of Borås, School of Business and IT.
    Johansson, Ulf
    University of Borås, School of Business and IT.
    Niklasson, Lars
    University of Borås, School of Business and IT.
    Instance Ranking Using Ensemble Spread2007Conference paper (Refereed)
    Abstract [en]

    This paper investigates a technique for predicting ensemble uncertainty originally proposed in the weather forecasting domain. The overall purpose is to find out if the technique can be modified to operate on a wider range of regression problems. The main difference, when moving outside the weather forecasting domain, is the lack of extensive statistical knowledge readily available for weather forecasting. In this study, three different modifications are suggested to the original technique. In the experiments, the modifications are compared to each other and to two straightforward technniques, using ten publicly available regression problems. Three of the techniques show promising result, especially one modification based on genetic algorithms. The suggested modification can accurately determine whether the confidence in ensemble predictions should be high or low.

  • 26.
    König, Rikard
    et al.
    University of Borås, School of Business and IT.
    Johansson, Ulf
    University of Borås, School of Business and IT.
    Niklasson, Lars
    The Importance of Representation Languages When Extracting Estimation Rules2007Conference paper (Refereed)
  • 27.
    König, Rikard
    et al.
    University of Borås, School of Business and IT.
    Johansson, Ulf
    University of Borås, School of Business and IT.
    Niklasson, Lars
    Using Genetic Programming to Increase Rule Quality2008In: In Proceedings of the Twenty-First International FLAIRS Conference, AAAI Press , 2008, p. 288-293Conference paper (Refereed)
    Abstract [en]

    Rule extraction is a technique aimed at transforming highly accurate opaque models like neural networks into comprehensible models without losing accuracy. G-REX is a rule extraction technique based on Genetic Programming that previously has performed well in several studies. This study has two objectives, to evaluate two new fitness functions for G-REX and to show how G-REX can be used as a rule inducer. The fitness functions are designed to optimize two alternative quality measures, area under ROC curves and a new comprehensibility measure called brevity. Rules with good brevity classifies typical instances with few and simple tests and use complex conditions only for atypical examples. Experiments using thirteen publicly available data sets show that the two novel fitness functions succeeded in increasing brevity and area under the ROC curve without sacrificing accuracy. When compared to a standard decision tree algorithm, G-REX achieved slightly higher accuracy, but also added additional quality to the rules by increasing their AUC or brevity significantly.

  • 28.
    König, Rikard
    et al.
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    Johansson, Ulf
    Jönköping University.
    Riveiro, Maria
    Högskolan i Skövde.
    Brattberg, Peter
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    Modeling Golf Player Skill Using Machine Learning2017In: International Cross-Domain Conference for Machine Learning and Knowledge Extraction: CD-MAKE 2017: Machine Learning and Knowledge Extraction, Calabri, 2017, p. 275-294Conference paper (Refereed)
    Abstract [en]

    In this study we apply machine learning techniques to Modeling Golf Player Skill using a dataset consisting of 277 golfers. The dataset includes 28 quantitative metrics, related to the club head at impact and ball flight, captured using a Doppler-radar. For modeling, cost-sensitive decision trees and random forest are used to discern between less skilled players and very good ones, i.e., Hackers and Pros. The results show that both random forest and decision trees achieve high predictive accuracy, with regards to true positive rate, accuracy and area under the ROC-curve. A detailed interpretation of the decision trees shows that they concur with modern swing theory, e.g., consistency is very important, while face angle, club path and dynamic loft are the most important evaluated swing factors, when discerning between Hackers and Pros. Most of the Hackers could be identified by a rather large deviation in one of these values compared to the Pros. Hackers, which had less variation in these aspects of the swing, could instead be identified by a steeper swing plane and a lower club speed. The importance of the swing plane is an interesting finding, since it was not expected and is not easy to explain.

  • 29.
    Radon, Anita
    et al.
    University of Borås, Faculty of Textiles, Engineering and Business.
    Johansson, Pia
    University of Borås, Faculty of Textiles, Engineering and Business.
    Sundström, Malin
    University of Borås, Faculty of Textiles, Engineering and Business.
    Alm, Håkan
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    Behre, Martin
    University of Borås, Faculty of Textiles, Engineering and Business.
    Göbel, Hannes
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    Hallqvist, Carina
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    Hernandez, Niina
    University of Borås, Faculty of Textiles, Engineering and Business.
    Hjelm-Lidholm, Sara
    University of Borås, Faculty of Textiles, Engineering and Business.
    König, Rikard
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    Lindberg, Ulla
    University of Borås, Faculty of Textiles, Engineering and Business.
    Löfström, Tuwe
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    Sundell, Håkan
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    Wallström, Stavroula
    University of Borås, Faculty of Textiles, Engineering and Business.
    What happens when retail meets research?: Special session2016Conference paper (Other academic)
    Abstract [en]

    special session Information

    We are witnessing the beginning of a seismic shift in retail due to digitalization. However, what is meant by digitalization is less clear. Sometimes it is understood as means for automatization and sometimes it is regarded as equal to e-commerce. Sometimes digitalization is considered being both automatization and e-commerce trough new technology. In recent years there has been an increase in Internet and mobile devise usage within the retail sector and e-commerce is growing, encompassing both large and small retailers. Digital tools such as, new applications are developing rapidly in order to search for information about products based on price, health, environmental and ethical considerations, and also to facilitate payments. Also the fixed store settings are changing due to digitalization and at an overall level; digitalization will lead to existing business models being reviewed, challenged and ultimately changed. More specifically, digitalization has consequences for all parts of the physical stores including customer interface, knowledge creation, sustainability performance and logistics. As with all major shifts, digitalization comprises both opportunities and challenges for retail firms and employees, and these needs to be empirically studied and systematically analysed. The Swedish Institute for Innovative Retailing at University of Borås is a research centre with the aim of identifying and analysing emerging trends that digitalization brings for the retail industry.

  • 30.
    Reveiro, Maria
    et al.
    Högskolan i Skövde.
    Dahlbom, Anders
    Högskolan i Skövde.
    König, Rikard
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    Johansson, Ulf
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    Brattberg, Peter
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    Supporting Golf Coaching and Swing Instruction with Computer-Based Training Systems2015In: Learning and Collaboration Technologies: Second International Conference, LCT 2015, Held as Part of HCI International 2015, Los Angeles, CA, USA, August 2-7, 2015, Proceedings / [ed] Panayiotis Zaphiris, Andri Ioannou, Los Angeles, 2015, Vol. 9192, p. 279-290Conference paper (Refereed)
    Abstract [en]

    Golf is a popular sport around the world. Since an accomplished golf swing is essential for succeeding in this sport, golf players spend a considerable amount of time perfecting their swing. In order to guide the design of future computer-based training systems that support swing instruction, this paper analyzes the data gathered during interviews with golf instructors and participant observations of actual swing coaching sessions. Based on our field work, we describe the characteristics of a proficient swing, how the instructional sessions are normally carried out and the challenges professional instructors face. Taking into account these challenges, we outline which desirable capabilities future computer-based training systems for professional golf instructors should have.

  • 31.
    Rikard, König
    et al.
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    Ulf, Johansson
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    Lindqvist, Ann
    Scania CV AB.
    Peter, Brattberg
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    Interesting Regression- and Model Trees Through Variable Restrictions2015In: Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 2015, p. 281-292Conference paper (Refereed)
    Abstract [en]

    The overall purpose of this paper is to suggest a new technique for creating interesting regression- and model trees. Interesting models are here defined as models that fulfill some domain dependent restriction of how variables can be used in the models. The suggested technique, named ReReM, is an extension of M5 which can enforce variable constraints while creating regression and model trees. To evaluate ReReM, two case studies were conducted where the first concerned modeling of golf player skill, and the second modeling of fuel consumption in trucks. Both case studies had variable constraints, defined by domain experts, that should be fulfilled for models to be deemed interesting. When used for modeling golf player skill, ReReM created regression trees that were slightly less accurate than M5’s regression trees. However, the models created with ReReM were deemed to be interesting by a golf teaching professional while the M5 models were not. In the second case study, ReReM was evalu ated against M5’s model trees and a semi-automated approach often used in the automotive industry. Here, experiments showed that ReReM could achieve a predictive performance comparable to M5 and clearly better than a semi-automated approach, while fulfilling the constraints regarding interesting models.

  • 32.
    Sundell, Håkan
    et al.
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    König, Rikard
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    Johansson, Ulf
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    Pragmatic Approach to Association Rule Learning in Real-World Scenarios2015Conference paper (Refereed)
  • 33.
    Sönströd, Cecilia
    et al.
    University of Borås, School of Business and IT.
    Johansson, Ulf
    University of Borås, School of Business and IT.
    König, Rikard
    University of Borås, School of Business and IT.
    Evolving Accurate and Comprehensible Classification Rules2011Conference paper (Refereed)
    Abstract [en]

    In this paper, Genetic Programming is used to evolve ordered rule sets (also called decision lists) for a number of benchmark classification problems, with evaluation of both predictive performance and comprehensibility. The main purpose is to compare this approach to the standard decision list algorithm JRip and also to evaluate the use of different length penalties and fitness functions for evolving this type of model. The results, using 25 data sets from the UCI repository, show that genetic decision lists with accuracy-based fitness functions outperform JRip regarding accuracy. Indeed, the best setup was significantly better than JRip. JRip, however, held a slight advantage over these models when evaluating AUC. Furthermore, all genetic decision list setups produced models that were more compact than JRip models, and thus more readily comprehensible. The effect of using different fitness functions was very clear; in essence, models performed best on the evaluation criterion that was used in the fitness function, with a worsening of the performance for other criteria. Brier score fitness provided a middle ground, with acceptable performance on both accuracy and AUC. The main conclusion is that genetic programming solves the task of evolving decision lists very well, but that different length penalties and fitness functions have immediate effects on the results. Thus, these parameters can be used to control the trade-off between different aspects of predictive performance and comprehensibility.

  • 34.
    Sönströd, Cecilia
    et al.
    University of Borås, School of Business and IT.
    Johansson, Ulf
    University of Borås, School of Business and IT.
    König, Rikard
    University of Borås, School of Business and IT.
    Niklasson, Lars
    Genetic Decision Lists for Concept Description2008In: Proceeding of The 2008 International Conference on Data Mining, CSREA Press , 2008, p. 450-457Conference paper (Refereed)
1 - 34 of 34
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