Feature Selection: A Practitioner View

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Saptarsi Goswami 1,* Amlan Chakrabarti 2

1. Institute of Engineering & Management, Kolkata, India

2. A.K.Choudhury School of Information and Technology, Calcutta University, Kolkata, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2014.11.10

Received: 21 Feb. 2014 / Revised: 2 May 2014 / Accepted: 18 Jul. 2014 / Published: 8 Oct. 2014

Index Terms

Feature Selection, Supervised, Unsupervised, Commercial, Application Domain


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.

Cite This Paper

Saptarsi Goswami, Amlan Chakrabarti, "Feature Selection: A Practitioner View", International Journal of Information Technology and Computer Science(IJITCS), vol.6, no.11, pp.66-77, 2014. DOI:10.5815/ijitcs.2014.11.10


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