Indian Journal of Science and Technology
Year: 2015, Volume: 8, Issue: 28, Pages: 1-5
M. Subathra1* and R. Nedunchezhian2
1 Department of Computer Applications, PSG College of Technology, Bharathiar University, Coimbatore – 641004, Tamil Nadu, India; [email protected]
2 KIT-Kalaignar Karunanidhi Institute of Technology, Coimbatore – 641402, Tamil Nadu, India; [email protected]
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
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