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The standard Homogeneity-Based (SHB) optimization algorithm is a metaheuristic which is proposed based on a simultaneously balance between fitting and generalization of a given classification system. However, the SHB algorithm does not penalize the structure of a classification model. This is due to the way SHB’s objective function is defined. Also, SHB algorithm uses only genetic algorithm to tune its parameters. This may reduce SHB’s freedom degree. In this paper we have proposed an Improved Homogeneity-Based Algorithm (IHBA) which adopts computational complexity of the used data mining approach. Additionally, we employs several metaheuristics to optimally find SHB’s parameters values. In order to prove the feasibility of the proposed approach, we conducted a computational study on some benchmarks datasets obtained from UCI repository. Experimental results confirm the theoretical analysis and show the effectiveness of the proposed IHBA method.

IHBA: An Improved Homogeneity-Based Algorithm for Data Classification