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Johansson, Ulf
Publications (10 of 71) Show all publications
Löfström, T., Johansson, U., Balkow, J. & Sundell, H. (2018). A data-driven approach to online fitting services. In: Jun Liu (Ulster University, UK), Jie Lu (University of Technology Sydney, Australia), Yang Xu (Southwest Jiaotong University, China), Luis Martinez (University of Jaén, Spain) and Etienne E Kerre (University of Ghent, Belgium) (Ed.), Data Science and Knowledge Engineering for Sensing Decision Support: . Paper presented at 13th International FLINS Conference, Belfast, August 21-24, 2018. (pp. 1559-1566).
Open this publication in new window or tab >>A data-driven approach to online fitting services
2018 (English)In: Data Science and Knowledge Engineering for Sensing Decision Support / [ed] Jun Liu (Ulster University, UK), Jie Lu (University of Technology Sydney, Australia), Yang Xu (Southwest Jiaotong University, China), Luis Martinez (University of Jaén, Spain) and Etienne E Kerre (University of Ghent, Belgium), 2018, p. 1559-1566Conference paper, Published paper (Refereed)
Abstract [en]

Being able to accurately predict several attributes related to size is vital for services supporting online fitting. In this paper, we investigate a data-driven approach, while comparing two different supervised modeling techniques for predictive regression; standard multiple linear regression and neural networks. Using a fairly large, publicly available, data set of high quality, the main results are somewhat discouraging. Specifically, it is questionable whether key attributes like sleeve length, neck size, waist and chest can be modeled accurately enough using easily accessible input variables as sex, weight and height. This is despite the fact that several services online offer exactly this functionality. For this specific task, the results show that standard linear regression was as accurate as the potentially more powerful neural networks. Most importantly, comparing the predictions to reasonable levels for acceptable errors, it was found that an overwhelming majority of all instances had at least one attribute with an unacceptably high prediction error. In fact, if requiring that all variables are predicted with an acceptable accuracy, less than 5% of all instances met that criterion. Specifically, for females, the success rate was as low as 1.8%.

Keywords
Predictive regression, online fitting, fashion
National Category
Computer Sciences
Research subject
Business and IT
Identifiers
urn:nbn:se:hb:diva-15824 (URN)10.1142/11069 (DOI)
Conference
13th International FLINS Conference, Belfast, August 21-24, 2018.
Projects
Datadriven innovation
Funder
Knowledge Foundation
Available from: 2019-02-25 Created: 2019-02-25 Last updated: 2019-02-25Bibliographically approved
Sundell, H., Löfström, T. & Johansson, U. (2018). Explorative multi-objective optimization of marketing campaigns for the fashion retail industry. In: Jun Liu, Jie Lu, Yang Xu, Luis Martinez and Etienne E Kerre (Ed.), Data Science and Knowledge Engineering for Sensing Decision Support: . Paper presented at FLINS 2018, Belfast, August 21-24, 2018. (pp. 1551-1558).
Open this publication in new window or tab >>Explorative multi-objective optimization of marketing campaigns for the fashion retail industry
2018 (English)In: Data Science and Knowledge Engineering for Sensing Decision Support / [ed] Jun Liu, Jie Lu, Yang Xu, Luis Martinez and Etienne E Kerre, 2018, p. 1551-1558Conference paper, Published paper (Refereed)
Abstract [en]

We show how an exploratory tool for association rule mining can be used for efficient multi-objective optimization of marketing campaigns for companies within the fashion retail industry. We have earlier designed and implemented a novel digital tool for mining of association rules from given basket data. The tool supports efficient finding of frequent itemsets over multiple hierarchies and interactive visualization of corresponding association rules together with numerical attributes. Normally when optimizing a marketing campaign, factors that cause an increased level of activation among the recipients could in fact reduce the profit, i.e., these factors need to be balanced, rather than optimized individually. Using the tool we can identify important factors that influence the search for an optimal campaign in respect to both activation and profit. We show empirical results from a real-world case-study using campaign data from a well-established company within the fashion retail industry, demonstrating how activation and profit can be simultaneously targeted, using computer-generated algorithms as well as human-controlled visualization.

Keywords
Association rules, marketing, visualization, Pareto front
National Category
Computer Sciences
Research subject
Business and IT
Identifiers
urn:nbn:se:hb:diva-15138 (URN)
Conference
FLINS 2018, Belfast, August 21-24, 2018.
Funder
Knowledge Foundation, 20160035
Available from: 2018-10-01 Created: 2018-10-01 Last updated: 2018-10-02Bibliographically approved
Johansson, U., Löfström, T., Sundell, H., Linnusson, H., Gidenstam, A. & Boström, H. (2018). Venn predictors for well-calibrated probability estimation trees. In: Alex J. Gammerman and Vladimir Vovk and Zhiyuan Luo and Evgueni N. Smirnov and Ralf L. M. Peeter (Ed.), 7th Symposium on Conformal and Probabilistic Prediction and Applications: COPA 2018, 11-13 June 2018, Maastricht, The Netherlands. Paper presented at 7th Symposium on Conformal and Probabilistic Prediction and Applications, London, June 11th - 13th, 2018 (pp. 3-14).
Open this publication in new window or tab >>Venn predictors for well-calibrated probability estimation trees
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2018 (English)In: 7th Symposium on Conformal and Probabilistic Prediction and Applications: COPA 2018, 11-13 June 2018, Maastricht, The Netherlands / [ed] Alex J. Gammerman and Vladimir Vovk and Zhiyuan Luo and Evgueni N. Smirnov and Ralf L. M. Peeter, 2018, p. 3-14Conference paper, Published paper (Refereed)
Abstract [en]

Successful use of probabilistic classification requires well-calibrated probability estimates, i.e., the predicted class probabilities must correspond to the true probabilities. The standard solution is to employ an additional step, transforming the outputs from a classifier into probability estimates. In this paper, Venn predictors are compared to Platt scaling and isotonic regression, for the purpose of producing well-calibrated probabilistic predictions from decision trees. The empirical investigation, using 22 publicly available datasets, showed that the probability estimates from the Venn predictor were extremely well-calibrated. In fact, in a direct comparison using the accepted reliability metric, the Venn predictor estimates were the most exact on every data set.

Series
Proceedings of Machine Learning Research
Keywords
Venn predictors, Calibration, Decision trees, Reliability
National Category
Computer Sciences
Research subject
Business and IT
Identifiers
urn:nbn:se:hb:diva-15061 (URN)
Conference
7th Symposium on Conformal and Probabilistic Prediction and Applications, London, June 11th - 13th, 2018
Funder
Knowledge Foundation
Available from: 2018-09-04 Created: 2018-09-04 Last updated: 2018-09-06Bibliographically approved
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
Linusson, H., Norinder, U., Boström, H., Johansson, U. & Löfström, T. (2017). On the Calibration of Aggregated Conformal Predictors. In: Proceedings of Machine Learning Research: . Paper presented at Conformal and Probabilistic Prediction and Applications, Stockholm Sweden 13-16 June, 2017.
Open this publication in new window or tab >>On the Calibration of Aggregated Conformal Predictors
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2017 (English)In: Proceedings of Machine Learning Research, 2017Conference paper, Published paper (Refereed)
Abstract [en]

Conformal prediction is a learning framework that produces models that associate witheach of their predictions a measure of statistically valid confidence. These models are typi-cally constructed on top of traditional machine learning algorithms. An important result ofconformal prediction theory is that the models produced are provably valid under relativelyweak assumptions—in particular, their validity is independent of the specific underlyinglearning algorithm on which they are based. Since validity is automatic, much research onconformal predictors has been focused on improving their informational and computationalefficiency. As part of the efforts in constructing efficient conformal predictors, aggregatedconformal predictors were developed, drawing inspiration from the field of classification andregression ensembles. Unlike early definitions of conformal prediction procedures, the va-lidity of aggregated conformal predictors is not fully understood—while it has been shownthat they might attain empirical exact validity under certain circumstances, their theo-retical validity is conditional on additional assumptions that require further clarification.In this paper, we show why validity is not automatic for aggregated conformal predictors,and provide a revised definition of aggregated conformal predictors that gains approximatevalidity conditional on properties of the underlying learning algorithm.

National Category
Computer Sciences
Identifiers
urn:nbn:se:hb:diva-13636 (URN)
Conference
Conformal and Probabilistic Prediction and Applications, Stockholm Sweden 13-16 June, 2017
Available from: 2018-02-09 Created: 2018-02-09 Last updated: 2018-03-14Bibliographically 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
Linusson, H., Johansson, U., Boström, H. & Löfström, T. (2016). Reliable Confidence Predictions Using Conformal Prediction. In: Lecture Notes in Computer Science: . Paper presented at PAKDD 2016: Advances in Knowledge Discovery and Data Mining, Auckland, April 19-22, 2016 (pp. 77-88). , 9651
Open this publication in new window or tab >>Reliable Confidence Predictions Using Conformal Prediction
2016 (Swedish)In: Lecture Notes in Computer Science, 2016, Vol. 9651, p. 77-88Conference paper, Published paper (Refereed)
Abstract [en]

Conformal classiers output condence prediction regions, i.e., multi-valued predictions that are guaranteed to contain the true output value of each test pattern with some predened probability. In order to fully utilize the predictions provided by a conformal classier, it is essential that those predictions are reliable, i.e., that a user is able to assess the quality of the predictions made. Although conformal classiers are statistically valid by default, the error probability of the prediction regions output are dependent on their size in such a way that smaller, and thus potentially more interesting, predictions are more likely to be incorrect. This paper proposes, and evaluates, a method for producing rened error probability estimates of prediction regions, that takes their size into account. The end result is a binary conformal condence predictor that is able to provide accurate error probability estimates for those prediction regions containing only a single class label.

National Category
Computer Sciences
Research subject
Bussiness and IT
Identifiers
urn:nbn:se:hb:diva-11963 (URN)
Conference
PAKDD 2016: Advances in Knowledge Discovery and Data Mining, Auckland, April 19-22, 2016
Available from: 2017-03-01 Created: 2017-03-01 Last updated: 2018-01-13Bibliographically approved
Löfström, T., Boström, H., Linusson, H. & Johansson, U. (2015). Bias Reduction through Conditional Conformal Prediction. Intelligent Data Analysis, 19(6), 1355-1375
Open this publication in new window or tab >>Bias Reduction through Conditional Conformal Prediction
2015 (English)In: Intelligent Data Analysis, ISSN 1088-467X, E-ISSN 1571-4128, Vol. 19, no 6, p. 1355-1375Article in journal (Refereed) In press
National Category
Computer Sciences
Identifiers
urn:nbn:se:hb:diva-756 (URN)10.3233/IDA-150786 (DOI)
Projects
High-Performance Data Mining for Drug Effect Detection (DADEL)
Available from: 2015-09-11 Created: 2015-09-11 Last updated: 2018-06-14Bibliographically 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
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