GP Classification under Imbalanced Data sets: Active Sub-sampling and AUC Approximation

Abstract

The problem of evolving binary classification models under increasingly unbalanced data sets is approached by proposing a strategy consisting of two components: Sub-sampling and ‘robust’ fitness function design. In particular, recent work in the wider machine learning literature has recognized that maintaining the original distribution of exemplars during… (More)
DOI: 10.1007/978-3-540-78671-9_23

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