Feature selection via maximizing global information gain for text classification.
In: Knowledge-Based Systems, Jg. 54 (2013-12-01), S. 298-309
Online
academicJournal
Highlights: [•] A novel feature selection metric called global information gain (GIG) is proposed. [•] An efficient algorithm called maximizing global information gain (MGIG) is developed. [•] MGIG performs better than other algorithms (IG, mRMR, JMI, DISR) in most cases. [•] MGIG runs significantly faster than mRMR, JMI and DISR, and comparable with IG. [Copyright &y& Elsevier]
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Feature selection via maximizing global information gain for text classification.
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Autor/in / Beteiligte Person: | Shang, Changxing ; Li, Min ; Feng, Shengzhong ; Jiang, Qingshan ; Fan, Jianping |
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Zeitschrift: | Knowledge-Based Systems, Jg. 54 (2013-12-01), S. 298-309 |
Veröffentlichung: | 2013 |
Medientyp: | academicJournal |
ISSN: | 0950-7051 (print) |
DOI: | 10.1016/j.knosys.2013.09.019 |
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