An Improved Alias Classification using Logistic Regression with Particle Swarm Optimization

M  Subathra   and R  Nedunchezhian

doi:10.17485/ijst/2015/v8i28/73900

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Indian Journal of Science and Technology

DOI: 10.17485/ijst/2015/v8i28/73900

Year: 2015, Volume: 8, Issue: 28, Pages: 1-5

Original Article

An Improved Alias Classification using Logistic Regression with Particle Swarm Optimization

M. Subathra^1* and R. Nedunchezhian²

¹Department of Computer Applications, PSG College of Technology, Bharathiar University, Coimbatore – 641004, Tamil Nadu, India; [email protected]
²KIT-Kalaignar Karunanidhi Institute of Technology, Coimbatore – 641402, Tamil Nadu, India; [email protected]

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

An improvement in detection of alias names of an entity is an important factor in many cases like terrorist and criminal network. In this paper, the social network properties are used to construct a feature set for classification. The proposed particle swarm optimization is used to optimize the regularization parameter of the logistic regression and improve the accuracy of the entity alias classification significantly to 4.98% compared to that of the logistic regression. The experimental results demonstrated its performance and the results are compared to the logistic regression with alias detection dataset.
Keywords: Alias Classification, Logistic Regression, Particle Swarm Optimization, Regularization