Document Type : Research Paper


Department of Computer Science, Faculty of Mathematical Sciences, University of Guilan, Rasht, Iran.


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


Main Subjects

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