A Variable Precision Attribute Reduction Approach in Multilabel Decision Tables

Abstract

Owing to the high dimensionality of multilabel data, feature selection in multilabel learning will be necessary in order to reduce the redundant features and improve the performance of multilabel classification. Rough set theory, as a valid mathematical tool for data analysis, has been widely applied to feature selection (also called attribute reduction). In this study, we propose a variable precision attribute reduct for multilabel data based on rough set theory, called δ-confidence reduct, which can correctly capture the uncertainty implied among labels. Furthermore, judgement theory and discernibility matrix associated with δ-confidence reduct are also introduced, from which we can obtain the approach to knowledge reduction in multilabel decision tables.