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High-probability minimax probability machines

Cousins, S; Shawe-Taylor, J; (2017) High-probability minimax probability machines. Machine Learning , 106 (6) pp. 863-886. 10.1007/s10994-016-5616-2. Green open access

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In this paper we focus on constructing binary classifiers that are built on the premise of minimising an upper bound on their future misclassification rate. We pay particular attention to the approach taken by the minimax probability machine (Lanckriet et al. in J Mach Learn Res 3:555–582, 2003), which directly minimises an upper bound on the future misclassification rate in a worst-case setting: that is, under all possible choices of class-conditional distributions with a given mean and covariance matrix. The validity of these bounds rests on the assumption that the means and covariance matrices are known in advance, however this is not always the case in practice and their empirical counterparts have to be used instead. This can result in erroneous upper bounds on the future misclassification rate and lead to the formulation of sub-optimal predictors. In this paper we address this oversight and study the influence that uncertainty in the moments, the mean and covariance matrix, has on the construction of predictors under the minimax principle. By using high-probability upper bounds on the deviation between true moments and their empirical counterparts, we can re-formulate the minimax optimisation to incorporate this uncertainty and find the predictor that minimises the high-probability, worst-case misclassification rate. The moment uncertainty introduces a natural regularisation component into the optimisation, where each class is regularised in proportion to the degree of moment uncertainty. Experimental results would support the view that in the case of with limited data availability, the incorporation of moment uncertainty can lead to the formation of better predictors.

Type: Article
Title: High-probability minimax probability machines
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/s10994-016-5616-2
Publisher version: http://doi.org/10.1007/s10994-016-5616-2
Language: English
Additional information: © 2017 The Author(s). This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Keywords: Classification; Regularisation; Minimax program
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/1542973
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