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Publication - Professor Peter Flach

    Background Check

    A General Technique to Build More Reliable and Versatile Classifiers

    Citation

    Perello-Nieto, M, Filho, TMS, Kull, M & Flach, P, 2017, ‘Background Check: A General Technique to Build More Reliable and Versatile Classifiers’. in: 2016 IEEE 16th International Conference on Data Mining (ICDM 2016): Proceedings of a meeting held 12-15 December 2016, Barcelona, Spain. Institute of Electrical and Electronics Engineers (IEEE), pp. 1143-1148

    Abstract

    We introduce a powerful technique to make classifiers more reliable and versatile. Background Check equips classifiers with the ability to assess the difference of unlabelled test data from the training data. In particular, Background Check gives classifiers the capability to (i) perform cautious classification with a reject option; (ii) identify outliers; and (iii) better assess the confidence in their predictions. We derive the method from first principles and consider four particular relationships between background and foreground distributions. One of these assumes an affine relationship with two parameters, and we show how this bivariate parameter space naturally interpolates between the above capabilities.We demonstrate the versatility of the approach by comparing it experimentally with published special-purpose solutions for outlier detection and confident classification on 41 benchmark datasets. Results show that Background Check can match and in many cases surpass the performances of specialised approaches.

    Full details in the University publications repository