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Hybrid ACO and TOFA feature selection approach for text classification

Tang, HL, Alghamdi, HS and Alshomrani, S (2012) Hybrid ACO and TOFA feature selection approach for text classification 2012 IEEE Congress on Evolutionary Computation, CEC 2012.

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Abstract

With the highly increasing availability of text data on the Internet, the process of selecting an appropriate set of features for text classification becomes more important, for not only reducing the dimensionality of the feature space, but also for improving the classification performance. This paper proposes a novel feature selection approach to improve the performance of text classifier based on an integration of Ant Colony Optimization algorithm (ACO) and Trace Oriented Feature Analysis (TOFA). ACO is metaheuristic search algorithm derived by the study of foraging behavior of real ants, specifically the pheromone communication to find the shortest path to the food source. TOFA is a unified optimization framework developed to integrate and unify several state-of-the-art dimension reduction algorithms through optimization framework. It has been shown in previous research that ACO is one of the promising approaches for optimization and feature selection problems. TOFA is capable of dealing with large scale text data and can be applied to several text analysis applications such as text classification, clustering and retrieval. For classification performance yet effective, the proposed approach makes use of TOFA and classifier performance as heuristic information of ACO. The results on Reuters and Brown public datasets demonstrate the effectiveness of the proposed approach. © 2012 IEEE.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences
Faculty of Engineering and Physical Sciences > Computing Science
Authors :
AuthorsEmailORCID
Tang, HLUNSPECIFIEDUNSPECIFIED
Alghamdi, HSUNSPECIFIEDUNSPECIFIED
Alshomrani, SUNSPECIFIEDUNSPECIFIED
Date : 2012
Identification Number : 10.1109/CEC.2012.6252960
Contributors :
ContributionNameEmailORCID
PublisherIEEE, UNSPECIFIEDUNSPECIFIED
Additional Information : © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Depositing User : Symplectic Elements
Date Deposited : 26 Sep 2013 10:52
Last Modified : 09 Jun 2014 13:12
URI: http://epubs.surrey.ac.uk/id/eprint/803209

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