By Topic

Harmony search based wrapper feature selection method for 1-nearest neighbour classifier

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
V. Krishnaveni ; Department of Computer Science Madurai Kamaraj University Madurai, Tamilnadu, India ; G. Arumugam

The high-dimensional feature vectors often impose a high computational cost when classification is performed. Feature selection plays major role as a pre-processing technique in reducing the dimensionality of the datasets in data analysis and data mining. This process reduces the number of features by removing irrelevant and redundant data and hence resulting in acceptable classification accuracy. Filter and wrapper are the two kinds of feature selection methods. Experimental results have proved that the wrapper methods can yield better performance, although they have the disadvantage of high computational cost. This paper presents a Harmony Search based novel optimization algorithm for wrapper feature selection. 1-NN classifier method has been used to evaluate the quality of the solutions. The performance of the proposed approach has been analysed by experiments with various real-world data sets. The proposed method, HS-1-NN, produced better performance than other state-of-the-art methods in terms of classification accuracy and convergence rate.

Published in:

Pattern Recognition, Informatics and Mobile Engineering (PRIME), 2013 International Conference on

Date of Conference:

21-22 Feb. 2013