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Feature Selection Algorithms for Classification and Clustering

Feature Selection Algorithms for Classification and Clustering

Arvind Kumar Tiwari
Copyright: © 2017 |Pages: 25
ISBN13: 9781522525455|ISBN10: 1522525459|EISBN13: 9781522525462
DOI: 10.4018/978-1-5225-2545-5.ch007
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MLA

Tiwari, Arvind Kumar. "Feature Selection Algorithms for Classification and Clustering." Ubiquitous Machine Learning and Its Applications, edited by Pradeep Kumar and Arvind Tiwari, IGI Global, 2017, pp. 143-167. https://doi.org/10.4018/978-1-5225-2545-5.ch007

APA

Tiwari, A. K. (2017). Feature Selection Algorithms for Classification and Clustering. In P. Kumar & A. Tiwari (Eds.), Ubiquitous Machine Learning and Its Applications (pp. 143-167). IGI Global. https://doi.org/10.4018/978-1-5225-2545-5.ch007

Chicago

Tiwari, Arvind Kumar. "Feature Selection Algorithms for Classification and Clustering." In Ubiquitous Machine Learning and Its Applications, edited by Pradeep Kumar and Arvind Tiwari, 143-167. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-2545-5.ch007

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Abstract

Feature selection is an important topic in data mining, especially for high dimensional dataset. Feature selection is a process commonly used in machine learning, wherein subsets of the features available from the data are selected for application of learning algorithm. The best subset contains the least number of dimensions that most contribute to accuracy. Feature selection methods can be decomposed into three main classes, one is filter method, another one is wrapper method and third one is embedded method. This chapter presents an empirical comparison of feature selection methods and its algorithm. In view of the substantial number of existing feature selection algorithms, the need arises to count on criteria that enable to adequately decide which algorithm to use in certain situation. This chapter reviews several fundamental algorithms found in the literature and assess their performance in a controlled scenario.

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