A New Approach to Multilabel Stratified Cross Validation with Application to Large and Sparse Gene Ontology Datasets
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Multilabel learning is an important topic in machine learning research. Evaluating models in multilabel settings requires specific cross validation methods designed for multilabel data. In this article, we show a weakness in an evaluation metric widely used in literature and we present improved versions of this metric and a general method, optisplit, for optimising cross validations splits. We present an extensive comparison of various types of cross validation methods in which we show that optisplit produces better cross validation splits than the existing methods and that it is fast enough to be used on big Gene Ontology (GO) datasets
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