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Subathra, M.
- A Novel Fuzzy Logic Model to Identify Closeness for Alias Detection
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Authors
Affiliations
1 Department of Computer Applications, PSG College of Technology, Bharathiar University, Coimbatore – 641004, Tamil Nadu, India, IN
2 KIT-Kalaignarkarunanidhi Institute of Technology, Coimbatore – 641402, Tamil Nadu, IN
1 Department of Computer Applications, PSG College of Technology, Bharathiar University, Coimbatore – 641004, Tamil Nadu, India, IN
2 KIT-Kalaignarkarunanidhi Institute of Technology, Coimbatore – 641402, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 8, No 28 (2015), Pagination:Abstract
The detection of person alias names are important for improving the accuracy of the information quality. The fuzzy-based decision system is proposed for alias detection, which is a rule-based system that uses fuzzy logic to make a decision about the closeness between the given name pairs. A fuzzy logic is formulated by a set of linguistic variables based on feature’s score value. An entity pair’s association score values are calculated using string and link-based features like Hamming Distance, Leventein Distance, Normalized String Edit Distance, Common Friends, Normalized Dot Product and Co-occurrence Relevance and an output variable accuracy as closeness. These features are transformed into fuzzy input variables and designed with proper membership functions. The proposed novel fuzzy inference system gives the decision of aliases closeness in the form of crisp values ranging from 0 to 1. In this work, the model achieves upto 90% accuracy compared to estimated accuracy.Keywords
Alias Detection, Closeness Identification, Fuzzy Logic, String and Link Based FeatureFull Text
- An Improved Alias Classification using Logistic Regression with Particle Swarm Optimization
Abstract Views :156 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Applications, PSG College of Technology, Bharathiar University, Coimbatore – 641004, Tamil Nadu, IN
2 KIT-Kalaignar Karunanidhi Institute of Technology, Coimbatore – 641402, Tamil Nadu, IN
1 Department of Computer Applications, PSG College of Technology, Bharathiar University, Coimbatore – 641004, Tamil Nadu, IN
2 KIT-Kalaignar Karunanidhi Institute of Technology, Coimbatore – 641402, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 8, No 28 (2015), Pagination: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 from Auton Lab.Keywords
Alias Classification, Logistic Regression, Particle Swarm Optimization RegularizationFull Text