Feature Selection: A Practitioner View

Feature Selection: A Practitioner View

Автор: Saptarsi Goswami, Amlan Chakrabarti

Журнал: International Journal of Information Technology and Computer Science(IJITCS) @ijitcs

Статья в выпуске: 11 Vol. 6, 2014 года.

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Feature selection is one of the most important preprocessing steps in data mining and knowledge Engineering. In this short review paper, apart from a brief taxonomy of current feature selection methods, we review feature selection methods that are being used in practice. Subsequently we produce a near comprehensive list of problems that have been solved using feature selection across technical and commercial domain. This can serve as a valuable tool to practitioners across industry and academia. We also present empirical results of filter based methods on various datasets. The empirical study covers task of classification, regression, text classification and clustering respectively. We also compare filter based ranking methods using rank correlation.

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Feature Selection, Supervised, Unsupervised, Commercial, Application Domain

Короткий адрес: https://sciup.org/15012194

IDR: 15012194

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