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  • 1.
    Johansson, Ulf
    et al.
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Department of Information Technology, University of Borås, Sweden.
    Löfström, Tuve
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Department of Information Technology, University of Borås, Sweden.
    Sundell, Håkan
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Department of Information Technology, University of Borås, Sweden.
    Venn predictors using lazy learners2018In: Proceedings of the 2018 International Conference on Data Science, ICDATA'18 / [ed] R. Stahlbock, G. M. Weiss & M. Abou-Nasr, CSREA Press, 2018, p. 220-226Conference paper (Refereed)
    Abstract [en]

    Probabilistic classification requires well-calibrated probability estimates, i.e., the predicted class probabilities must correspond to the true probabilities. Venn predictors, which can be used on top of any classifier, are automatically valid multiprobability predictors, making them extremely suitable for probabilistic classification. A Venn predictor outputs multiple probabilities for each label, so the predicted label is associated with a probability interval. While all Venn predictors are valid, their accuracy and the size of the probability interval are dependent on both the underlying model and some interior design choices. Specifically, all Venn predictors use so called Venn taxonomies for dividing the instances into a number of categories, each such taxonomy defining a different Venn predictor. A frequently used, but very basic taxonomy, is to categorize the instances based on their predicted label. In this paper, we investigate some more finegrained taxonomies, that use not only the predicted label but also some measures related to the confidence in individual predictions. The empirical investigation, using 22 publicly available data sets and lazy learners (kNN) as the underlying models, showed that the probability estimates from the Venn predictors, as expected, were extremely well-calibrated. Most importantly, using the basic (i.e., label-based) taxonomy produced significantly more accurate and informative Venn predictors compared to the more complex alternatives. In addition, the results also showed that when using lazy learners as underlying models, a transductive approach significantly outperformed an inductive, with regard to accuracy and informativeness. This result is in contrast to previous studies, where other underlying models were used.

  • 2.
    Johansson, Ulf
    et al.
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Löfström, Tuwe
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Sundell, Håkan
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Linusson, Henrik
    Department of Information Technology, University of Borås, Sweden.
    Gidenstam, Anders
    Department of Information Technology, University of Borås, Sweden.
    Boström, Henrik
    School of Information and Communication Technology, Royal Institute of Technology, Sweden.
    Venn predictors for well-calibrated probability estimation trees2018In: Conformal and Probabilistic Prediction and Applications / [ed] A. Gammerman, V. Vovk, Z. Luo, E. Smirnov, & R. Peeters, 2018, p. 3-14Conference 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 data sets, 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.

  • 3.
    Johansson, Ulf
    et al.
    Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT.
    Sundström, Malin
    Högskolan i Borås, Akademin för textil, teknik och ekonomi.
    Sundell, Håkan
    Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT.
    Rickard, König
    Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT.
    Jenny, Balkow
    Högskolan i Borås, Akademin för textil, teknik och ekonomi.
    Dataanalys för ökad kundförståelse2016Report (Other (popular science, discussion, etc.))
  • 4.
    Löfström, Tuwe
    et al.
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Univ Boras, Dept Informat Technol, Boras, Sweden.
    Johansson, Ulf
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Univ Boras, Dept Informat Technol, Boras, Sweden.
    Balkow, Jenny
    Univ Boras, Swedish Sch Text, Boras, Sweden.
    Sundell, Håkan
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Univ Boras, Dept Informat Technol, Boras, Sweden.
    A data-driven approach to online fitting services2018In: Data Science And Knowledge Engineering For Sensing Decision Support / [ed] Liu, J, Lu, J, Xu, Y, Martinez, L & Kerre, EE, World Scientific, 2018, Vol. 11, p. 1559-1566Conference 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 %.

  • 5.
    Sundell, Håkan
    et al.
    Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT.
    König, Rikard
    Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT.
    Johansson, Ulf
    Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT.
    Pragmatic Approach to Association Rule Learning in Real-World Scenarios2015Conference paper (Refereed)
    Abstract [en]

    We present a pragmatic approach for designing an efficient tool for extracting knowledge from customer data in the retail industry, e.g. market basket analysis. Association rule learning is an established topic within data mining and knowledge discovery with a large interest from the business intelligence community. With a focus on properties from a real-world environment and with an aim to get customer insights on a cross-hierarchy level, we have chosen to build upon the common Apriori algorithm. This algorithm has been optimized for the chosen real-world environment and adapted for implementation on commonly available computing platforms and workstations using the Microsoft .net framework. Several parallelization strategies have been developed and experimental results indicate that a significant speed-up is possible and that the tool can be utilized for producing valuable information.

  • 6.
    Sundell, Håkan
    et al.
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Univ Boras, Dept Informat Technol, Boras, Sweden.
    Löfström, Tuwe
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Univ Boras, Dept Informat Technol, Boras, Sweden.
    Johansson, Ulf
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Univ Boras, Dept Informat Technol, Boras, Sweden.
    Explorative multi-objective optimization of marketing campaigns for the fashion retail industry2018In: Data Science And Knowledge Engineering For Sensing Decision Support / [ed] Liu, J, Lu, J, Xu, Y, Martinez, L & Kerre, EE, World Scientific, 2018, Vol. 11, p. 1551-1558Conference 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 pro fit. 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.

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