Research Papers
A Meta-Heuristic Regression-Based Feature Selection for Predictive Analytics
Authors:
Bharat Singh ,
Department of Information Technology, Indian Institute of Information Technology, Allahabad, India, IN
O P Vyas
Department of Information Technology, Indian Institute of Information Technology, Allahabad, India, IN
Abstract
A high-dimensional feature selection having a very large number of features with an optimal feature subset is an NP-complete problem. Because conventional optimization techniques are unable to tackle large-scale feature selection problems, meta-heuristic algorithms are widely used. In this paper, we propose a particle swarm optimization technique while utilizing regression techniques for feature selection. We then use the selected features to classify the data. Classification accuracy is used as a criterion to evaluate classifier performance, and classification is accomplished through the use of k-nearest neighbour (KNN) and Bayesian techniques. Various high dimensional data sets are used to evaluate the usefulness of the proposed approach. Results show that our approach gives better results when compared with other conventional feature selection algorithms.
How to Cite:
Singh, B. and Vyas, O.P., 2014. A Meta-Heuristic Regression-Based Feature Selection for Predictive Analytics. Data Science Journal, 13, pp.106–118. DOI: http://doi.org/10.2481/dsj.14-032
Published on
06 Nov 2014.
Peer Reviewed
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