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Erschienen in:
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2014 | OriginalPaper | Buchkapitel

A Survey on Filter Techniques for Feature Selection in Text Mining

verfasst von : Kusum Kumari Bharti, Pramod kumar Singh

Erschienen in: Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), December 28-30, 2012

Verlag: Springer India

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Abstract

A large portion of a document is usually covered by irrelevant features. Instead of identifying actual context of the document, such features increase dimensions in the representation model and computational complexity of underlying algorithm, and hence adversely affect the performance. It necessitates a requirement of relevant feature selection in the given feature space. In this context, feature selection plays a key role in removing irrelevant features from the original feature space. Feature selection methods are broadly categorized into three groups: filter, wrapper, and embedded. Filter methods are widely used in text mining because of their simplicity, computational complexity, and efficiency. In this article, we provide a brief survey of filter feature selection methods along with some of the recent developments in this area.

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Vorheriges Kapitel Social Evolution: An Evolutionary Algorithm Inspired by Human Interactions
Nächstes Kapitel An Effective Hybrid Method Based on DE, GA, and K-means for Data Clustering
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Metadaten
Titel
A Survey on Filter Techniques for Feature Selection in Text Mining
verfasst von
Kusum Kumari Bharti
Pramod kumar Singh
Copyright-Jahr
2014
Verlag
Springer India
Buch
Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), December 28-30, 2012

Print ISBN: 978-81-322-1601-8

Electronic ISBN: 978-81-322-1602-5

Copyright-Jahr: 2014

DOI
https://doi.org/10.1007/978-81-322-1602-5_154

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