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A Dimension Reduction Approach to Classification Based on Particle Swarm Optimisation and Rough Set Theory
Liam Cervante1, Bing Xue1, Lin Shang2, and Mengjie Zhang1
1Victoria University of Wellington, PO Box 600, Wellington 6140, New Zealand
Liam.Cervante@ecs.vuw.ac.nz
Bing.Xue@ecs.vuw.ac.nz
Mengjie.Zhang@ecs.vuw.ac.nz
2State Key Laboratory of Novel Software Technology, Nanjing University, Nanjing 210046, China
shanglin@nju.edu.cn
Abstract. Dimension reduction aims to remove unnecessary attributes from datasets to overcome the problem of “the curse of dimensionality”, which is an obstacle in classification. Based on the analysis of the limitations of the standard rough set theory, we propose a new dimension reduction approach based on binary particle swarm optimisation (BPSO) and probabilistic rough set theory. The new approach includes two new specific algorithms, which are PSOPRS using only the probabilistic rough set in the fitness function and PSOPRSN adding the number of attributes in the fitness function. Decision trees, naive Bayes and nearest neighbour algorithms are employed to evaluate the classification accuracy of the reduct achieved by the proposed algorithms on five datasets. Experimental results show that the two new algorithms outperform the algorithm using BPSO with standard rough set and two traditional dimension reduction algorithms. PSOPRSN obtains a smaller number of attributes than PSOPRS with the same or slightly worse classification performance. This work represents the first study on probabilistic rough set for for filter dimension reduction in classification problems.
Keywords: Dimension reduction, Particle Swarm Optimisation, Filter Approaches, Classification
LNAI 7691, p. 313 ff.
lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2012