Multi-Objective Cuckoo Search Optimization for Dimensionality Reduction

Commonly, attributes in data sets are originally correlated, noisy and redundant. Thus, attribute reduction is a challenging task as it substantially affects the overall classification accuracy. In this research, a system for attribute reduction was proposed using correlation-based filter model for attribute reduction. The cuckoo search (CS) optimization algorithm was utilized to search the attribute space with minimum correlation among selected attributes. Then, the initially selected solutions, guaranteed to have minor correlation, are candidates for further improvement towards the classification accuracy fitness function. The performance of the proposed system has been tested via implementing it using various data sets. Also, its performance have has been compared against other common attribute reduction algorithms. Experimental results showed that the proposed multi-objective CS system has outperformed the typical single-objective CS optimizer as well as outperforming both the particle swarm optimization (PSO) and genetic algorithm (GA) optimization algorithms.