PyIT-MLFS: a Python-based information theoretical multi-label feature selection library
In: International Journal of Research in Industrial Engineering, Jg. 11 (2022), Heft 1, S. 9-15
academicJournal
Zugriff:
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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PyIT-MLFS: a Python-based information theoretical multi-label feature selection library
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Autor/in / Beteiligte Person: | Eskandari, Sadegh |
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Zeitschrift: | International Journal of Research in Industrial Engineering, Jg. 11 (2022), Heft 1, S. 9-15 |
Veröffentlichung: | Ayandegan Institute of Higher Education, 2022 |
Medientyp: | academicJournal |
ISSN: | 2783-1337 (print) ; 2717-2937 (print) |
DOI: | 10.22105/riej.2022.308916.1252 |
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