TITLE: Novel split quality measures for stratified multilabel Cross Validation with application to large and sparse gene ontology datasets 

ABSTRACT: 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 that the most
widely used cross validation split quality measures do not behave adequately
with multilabel data that has strong class imbalance. We present improved
measures and an algorithm, 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 more even cross validation splits than
the existing methods and that it is fast enough to be used on big Gene Ontology
(GO) datasets.
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 that the most
widely used cross validation split quality measures do not behave adequately
with multilabel data that has strong class imbalance. We present improved
measures and an algorithm, 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 more even cross validation splits than
the existing methods and that it is fast enough to be used on big Gene Ontology
(GO) datasets.
Novel split quality measures for stratified multilabel Cross Validation with application to large and sparse gene ontology datasets
http://arxiv.org/abs/2109.01425v2