Optimization techniques for feature selection in classification

  Dr. K. James Mathai ,  Kshiti Agnihotri

the feature selection process is considered a problem of global combinatorial optimization technique that aims to reduce the number of features, removes irrelevant, noisy and redundant data and results in standard classification accuracy. Feature selection plays key role in machine learning, pattern classification and data mining applications. Therefore, a good feature selection method is needed based on the number of features investigated for sample classification in order to speed up the processing, to reduce the time complexity ,to rate predictive accuracy, and to reduce computational complexity. Although a large body of research has delved into this problem, there is a paucity of survey that indicates trends and directions. This paper attempts to categorize the prevalent popular optimization techniques in feature selection that deals with enhancing classification performance in terms of accuracy and efficiency.

Keywords- Classification, Feature selection, Dimensionality reduction, Feature subset.

Unique Identification Number - IJEDR1703165
Page Number(s) - 1167-1170
Pubished in - Volume 5 | Issue 3 | September 2017
DOI (Digital Object Identifier) -   
Publisher - IJEDR (ISSN - 2321-9939)

  Dr. K. James Mathai ,  Kshiti Agnihotri,   "Optimization techniques for feature selection in classification", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.5, Issue 3, pp.1167-1170, September 2017, Available at :http://www.ijedr.org/papers/IJEDR1703165.pdf