A Dimension Reduction Approach to Classification Based on Particle Swarm Optimisation and Rough Set Theory

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.