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König, Rikard
Publications (10 of 34) Show all publications
König, R., Johansson, U., Riveiro, M. & Brattberg, P. (2017). Modeling Golf Player Skill Using Machine Learning. In: International Cross-Domain Conference for Machine Learning and Knowledge Extraction: CD-MAKE 2017: Machine Learning and Knowledge Extraction. Paper presented at International Cross-Domain Conference, Reggio Italy, August 29 – September 1, 2017. (pp. 275-294). Calabri
Open this publication in new window or tab >>Modeling Golf Player Skill Using Machine Learning
2017 (English)In: International Cross-Domain Conference for Machine Learning and Knowledge Extraction: CD-MAKE 2017: Machine Learning and Knowledge Extraction, Calabri, 2017, p. 275-294Conference paper, Published 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.

Place, publisher, year, edition, pages
Calabri: , 2017
Series
Lecture Notes in Computer Science ; 10410
Keywords
Classification, Decision trees, Machine learning, Golf, Swing analysis
National Category
Computer Sciences
Research subject
Business and IT
Identifiers
urn:nbn:se:hb:diva-13938 (URN)10.1007/978-3-319-66808-6_19 (DOI)978-3-319-66807-9 (ISBN)
Conference
International Cross-Domain Conference, Reggio Italy, August 29 – September 1, 2017.
Projects
TIKT2 - GOATS - Golf Data Analytics
Funder
Region Västra Götaland
Available from: 2018-04-04 Created: 2018-04-04 Last updated: 2018-04-19Bibliographically approved
Johansson, U., Sundström, M., Håkan, S., Rickard, K. & Jenny, B. (2016). Dataanalys för ökad kundförståelse. Stockholm: Handelsrådet
Open this publication in new window or tab >>Dataanalys för ökad kundförståelse
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2016 (Swedish)Report (Other (popular science, discussion, etc.))
Place, publisher, year, edition, pages
Stockholm: Handelsrådet, 2016. p. 66
National Category
Business Administration
Identifiers
urn:nbn:se:hb:diva-12080 (URN)
Available from: 2017-03-31 Created: 2017-03-31 Last updated: 2017-05-02Bibliographically approved
Radon, A., Johansson, P., Sundström, M., Alm, H., Behre, M., Göbel, H., . . . Wallström, S. (2016). What happens when retail meets research?: Special session. In: : . Paper presented at ANZMAC Conference 2016 - Marketing in a Post-Disciplinary Era, Christchurch, 5-7 December, 2016.
Open this publication in new window or tab >>What happens when retail meets research?: Special session
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2016 (English)Conference paper, Oral presentation with published abstract (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.

National Category
Economics and Business
Research subject
Bussiness and IT
Identifiers
urn:nbn:se:hb:diva-11892 (URN)
Conference
ANZMAC Conference 2016 - Marketing in a Post-Disciplinary Era, Christchurch, 5-7 December, 2016
Available from: 2017-02-08 Created: 2017-02-08 Last updated: 2017-05-02Bibliographically approved
Rikard, K., Ulf, J., Lindqvist, A. & Peter, B. (2015). Interesting Regression- and Model Trees Through Variable Restrictions. In: Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management: . Paper presented at International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, Lisbon, November 12-14, 2015. (pp. 281-292).
Open this publication in new window or tab >>Interesting Regression- and Model Trees Through Variable Restrictions
2015 (English)In: Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 2015, p. 281-292Conference paper, Published 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.

Keywords
Predictive Modeling, Model Trees, Interestingness, Regression, Vehicle modeling, Golf
National Category
Computer Sciences
Research subject
Business and IT
Identifiers
urn:nbn:se:hb:diva-8847 (URN)10.5220/0005600302810292 (DOI)978-989-758-158-8 (ISBN)
Conference
International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, Lisbon, November 12-14, 2015.
Projects
Big Data Analytics by OnlineEnsemble Learning (BOEL)Golf data analysis (GOATS)
Funder
Knowledge Foundation, 20120192
Available from: 2016-02-12 Created: 2016-02-12 Last updated: 2018-06-01Bibliographically approved
Sundell, H., König, R. & Johansson, U. (2015). Pragmatic Approach to Association Rule Learning in Real-World Scenarios. In: : . Paper presented at The 2015 International Conference on Computational Science and Computational Intelligence (CSCI'15).
Open this publication in new window or tab >>Pragmatic Approach to Association Rule Learning in Real-World Scenarios
2015 (English)Conference paper, Published paper (Refereed)
National Category
Computer Sciences
Research subject
Bussiness and IT
Identifiers
urn:nbn:se:hb:diva-8515 (URN)
Conference
The 2015 International Conference on Computational Science and Computational Intelligence (CSCI'15)
Available from: 2016-01-14 Created: 2016-01-14 Last updated: 2018-01-10Bibliographically approved
Reveiro, M., Dahlbom, A., König, R., Johansson, U. & Brattberg, P. (2015). Supporting Golf Coaching and Swing Instruction with Computer-Based Training Systems. In: Panayiotis Zaphiris, Andri Ioannou (Ed.), 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. Paper presented at Learning and Collaboration Technologies, Second International Conference, Los Angeles USA, August 2-7, 2015. (pp. 279-290). Los Angeles, 9192
Open this publication in new window or tab >>Supporting Golf Coaching and Swing Instruction with Computer-Based Training Systems
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2015 (English)In: 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, Published 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.

Place, publisher, year, edition, pages
Los Angeles: , 2015
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 9192
Keywords
Golf, Swing instruction, Computer-based training systems
National Category
Computer Sciences
Research subject
Business and IT
Identifiers
urn:nbn:se:hb:diva-8848 (URN)10.1007/978-3-319-20609-7_27 (DOI)978-3-319-20609-7 (ISBN)
Conference
Learning and Collaboration Technologies, Second International Conference, Los Angeles USA, August 2-7, 2015.
Projects
Golf data analysis (GOATS)
Available from: 2016-02-12 Created: 2016-02-12 Last updated: 2018-06-02Bibliographically approved
Johansson, U., Sönströd, C. & König, R. (2014). Accurate and Interpretable Regression Trees using Oracle Coaching. Paper presented at 5th IEEE Symposium Computational Intelligence and Data Mining, 9-12 Decmber, Orlando, FL, USA. Paper presented at 5th IEEE Symposium Computational Intelligence and Data Mining, 9-12 Decmber, Orlando, FL, USA. IEEE
Open this publication in new window or tab >>Accurate and Interpretable Regression Trees using Oracle Coaching
2014 (English)Conference paper, Published 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.

Place, publisher, year, edition, pages
IEEE, 2014
Keywords
Oracle coaching, Regression trees, Predictive modeling, Interpretable models, Machine learning, Data mining
National Category
Computer Sciences Computer and Information Sciences
Identifiers
urn:nbn:se:hb:diva-7319 (URN)2320/14712 (Local ID)978-1-4799-4518-4/14 (ISBN)2320/14712 (Archive number)2320/14712 (OAI)
Conference
5th IEEE Symposium Computational Intelligence and Data Mining, 9-12 Decmber, Orlando, FL, USA
Note

Sponsorship:

This work was supported by the Swedish Foundation for Strategic

Research through the project High-Performance Data Mining for Drug Effect

Detection (IIS11-0053), the Swedish Retail and Wholesale Development

Council through the project Innovative Business Intelligence Tools (2013:5)

and the Knowledge Foundation through the project Big Data Analytics by

Online Ensemble Learning (20120192).

Available from: 2015-12-22 Created: 2015-12-22 Last updated: 2018-01-10
Gabrielsson, P., Johansson, U. & König, R. (2014). Co-Evolving Online High-Frequency Trading Strategies Using Grammatical Evolution. Paper presented at IEEE Conference on Computational Intelligence for Financial Engineering & Economics, 27-28 March, 2014, London, UK. Paper presented at IEEE Conference on Computational Intelligence for Financial Engineering & Economics, 27-28 March, 2014, London, UK. IEEE
Open this publication in new window or tab >>Co-Evolving Online High-Frequency Trading Strategies Using Grammatical Evolution
2014 (English)Conference paper, Published 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.

Place, publisher, year, edition, pages
IEEE, 2014
Keywords
Grammatical evolution, High-frequency trading, Machine learning, Data mining
National Category
Computer Sciences Computer and Information Sciences
Identifiers
urn:nbn:se:hb:diva-7321 (URN)10.1109/CIFEr.2014.6924111 (DOI)2320/14713 (Local ID)2320/14713 (Archive number)2320/14713 (OAI)
Conference
IEEE Conference on Computational Intelligence for Financial Engineering & Economics, 27-28 March, 2014, London, UK
Note
Best paper award.Available from: 2015-12-22 Created: 2015-12-22 Last updated: 2018-01-10
König, R. (2014). Enhancing genetic programming for predictive modeling. (Doctoral dissertation). Örebro universitet
Open this publication in new window or tab >>Enhancing genetic programming for predictive modeling
2014 (English)Doctoral thesis, monograph (Other academic)
Place, publisher, year, edition, pages
Örebro universitet, 2014
Series
Örebro Studies in Technology, ISSN 1650-8580 ; 58
Keywords
Genetic programming, Predictive Modeling, Data Mining, Machine Learning, Rule extraction, Classification, Regression, Ensembles, Comprehensibility, Datavetenskap
National Category
Computer and Information Sciences Computer Sciences
Identifiers
urn:nbn:se:hb:diva-3689 (URN)2320/13423 (Local ID)978-91-7529-001-0 (ISBN)2320/13423 (Archive number)2320/13423 (OAI)
Note

Avhandling för teknologie doktorsexamen i datavetenskap, som kommer att försvaras offentligt tisdagen den 11 mars 2014 kl. 13.15, M404, Högskolan i Borås. Opponent: docent Niklas Lavesson, Blekinge Tekniska Högskola, Karlskrona.

Available from: 2015-12-04 Created: 2015-12-04 Last updated: 2018-01-10Bibliographically approved
König, R. & Johansson, U. (2014). Rule Extraction using Genetic Programming for Accurate Sales Forecasting. Paper presented at 5th IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2014), 9-12 december, Orlando, FL, USA. Paper presented at 5th IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2014), 9-12 december, Orlando, FL, USA. IEEE
Open this publication in new window or tab >>Rule Extraction using Genetic Programming for Accurate Sales Forecasting
2014 (English)Conference paper, Published 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.

Place, publisher, year, edition, pages
IEEE, 2014
Keywords
Genetic programming, Rule extraction, Overfitting, Regression, Sales forecasting, Machine learning, Data mining
National Category
Computer Sciences Computer and Information Sciences
Identifiers
urn:nbn:se:hb:diva-7320 (URN)2320/14624 (Local ID)978-1-4799-4518-4/14 (ISBN)2320/14624 (Archive number)2320/14624 (OAI)
Conference
5th IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2014), 9-12 december, Orlando, FL, USA
Note

Sponsorship:

This work was supported by the Swedish Retail and Wholesale Development

Council through the project Innovative Business Intelligence Tools (2013:5).

Available from: 2015-12-22 Created: 2015-12-22 Last updated: 2018-01-10
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