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A benchmarking study of classification techniques for behavioral data


  • DE CNUDDE, Sofie
  • MARTENS, David
  • EVGENIOU, Theodoros
  • PROVOST, Foster


The predictive power in ubiquitous big, behavioral data has been emphasized by previous academic research. The ultra-high dimensional and sparse characteristics, however, pose significant challenges on state-of-the-art classification techniques. Moreover, no consensus exists regarding a feasible trade-off between classification performance and computational complexity. This work provides a contribution in this direction through a systematic benchmarking study. Forty-three fine-grained behavioral data sets are analyzed with 11 classification techniques. Statistical performance comparisons enriched with learning curve analyses demonstrate two important findings. Firstly, an inherent AUC-time trade-off becomes clear, making the choice for an appropriate classifier dependent on time restrictions and data set characteristics. Logistic regression achieves the best AUC, however in the worst amount of time. Also, L2 regularization proves better than sparse L1-regularization. An attractive trade-off is found in a similarity-based technique called PSN. Secondly, the results illustrate that significant value lies in collecting and analyzing even more data, both in the instance and in the feature dimension, contrasting findings on traditional data. The results of this study provide guidance for researchers and practitioners for the selection of appropriate classification techniques, sample sizes and data features, while also providing focus in scalable algorithm design in the face of large, behavioral data.

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  • DE CNUDDE, Sofie & MARTENS, David & EVGENIOU, Theodoros & PROVOST, Foster, 2017. "A benchmarking study of classification techniques for behavioral data," Working Papers 2017005, University of Antwerp, Faculty of Applied Economics.
  • Handle: RePEc:ant:wpaper:2017005

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    References listed on IDEAS

    1. K. W. De Bock & D. Van Den Poel & S. Manigart, 2009. "Predicting web site audience demographics for web advertising targeting using multi-web site clickstream data," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 09/618, Ghent University, Faculty of Economics and Business Administration.
    2. repec:wsi:ijitdm:v:05:y:2006:i:04:n:s0219622006002258 is not listed on IDEAS
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    1. DE CNUDDE, Sofie & MARTENS, David & PROVOST, Foster, 2018. "An exploratory study towards applying and demystifying deep learning classification on behavioral big data," Working Papers 2018002, University of Antwerp, Faculty of Applied Economics.

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