pyit-mlfs: a python-based information theoretical multi-label feature selection library

Multi-label learning is an emerging research direction that deals with data in which an instance may belong to multiple class labels simultaneously. as many multi-label data contain very large feature space with hundreds of irrelevant and redundant features, multi-label feature selection is a fundamental pre-processing tool for selecting a subset of most representative and discriminative features. this paper introduces a python-based open-source library that provides the state-ofthe-art information theoretical filter-based multi-label feature selection algorithms. the library, called pyit-mlfs, is designed to facilitate the development of new algorithms. it is the first comprehensive open-source library for implementing algorithms of multilabel feature selection. moreover, it provides a high-level interface that enables the end-users to test and compare different already implemented algorithms. pyit-mlfs is available from https://github.com/sadegh28/pyit-mlfs.