IHBA: An Improved Homogeneity-Based Algorithm for Data Classification

Abstract

Abstract. 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.

An improved HBA metaheuristic

“…In medical domain, the goal is to propose a classifier model that can automatically make predictions (Al Muhaideb and Menai, 2013;Kotsiantis, 2007) from existing data samples, which can assist enormously physicians in the diagnosis process, based on previous knowledge, present in the medical dataset. Recently, metaheuristics have been widely employed for solving medical data classification problem (Bekaddour and Amine, 2015).…”