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 |
| _version_ | 1828399975267041280 |
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| author | Sadegh Eskandari |
| author_facet | Sadegh Eskandari |
| author_sort | Sadegh Eskandari |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T09:24:35Z |
| format | Article |
| id | doaj.art-2c590138f62c44c3a45064388c097ff0 |
| institution | Directory Open Access Journal |
| issn | 2783-1337 2717-2937 |
| language | English |
| last_indexed | 2024-12-10T09:24:35Z |
| publishDate | 2022-03-01 |
| publisher | Ayandegan Institute of Higher Education, |
| record_format | Article |
| series | International Journal of Research in Industrial Engineering |
| spelling | doaj.art-2c590138f62c44c3a45064388c097ff02022-12-22T01:54:34ZengAyandegan Institute of Higher Education,International Journal of Research in Industrial Engineering2783-13372717-29372022-03-0111191510.22105/riej.2022.308916.1252144057PyIT-MLFS: a Python-based information theoretical multi-label feature selection librarySadegh Eskandari0Department 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 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.http://www.riejournal.com/article_144057_d784382d18541832f86049e1bc2fbe02.pdffeature selectionmulti-label learning librarydata mining |
| spellingShingle | Sadegh Eskandari PyIT-MLFS: a Python-based information theoretical multi-label feature selection library International Journal of Research in Industrial Engineering feature selection multi-label learning library data mining |
| title | PyIT-MLFS: a Python-based information theoretical multi-label feature selection library |
| title_full | PyIT-MLFS: a Python-based information theoretical multi-label feature selection library |
| title_fullStr | PyIT-MLFS: a Python-based information theoretical multi-label feature selection library |
| title_full_unstemmed | PyIT-MLFS: a Python-based information theoretical multi-label feature selection library |
| title_short | PyIT-MLFS: a Python-based information theoretical multi-label feature selection library |
| title_sort | pyit mlfs a python based information theoretical multi label feature selection library |
| topic | feature selection multi-label learning library data mining |
| url | http://www.riejournal.com/article_144057_d784382d18541832f86049e1bc2fbe02.pdf |
| work_keys_str_mv | AT sadegheskandari pyitmlfsapythonbasedinformationtheoreticalmultilabelfeatureselectionlibrary |