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 andredundant features, multi-label feature selection is a fundame...
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| Format: | Article |
| Language: | English |
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Ayandegan Institute of Higher Education,
2022-03-01
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| Series: | International Journal of Research in Industrial Engineering |
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| Online Access: | http://www.riejournal.com/article_144057_d784382d18541832f86049e1bc2fbe02.pdf |
| Summary: | 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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| ISSN: | 2783-1337 2717-2937 |