International Journal of Research in Industrial Engineering (Mar 2022)

PyIT-MLFS: a Python-based information theoretical multi-label feature selection library

  • Sadegh Eskandari

DOI
https://doi.org/10.22105/riej.2022.308916.1252
Journal volume & issue
Vol. 11, no. 1
pp. 9 – 15

Abstract

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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 andredundant 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.

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