An improved HBA metaheuristic

Abstract

As simple and effective optimisation approach, homogeneity-based algorithm (HBA) is one of the recent metaheuristics, proposed to minimise the total misclassification cost of data mining approaches. However, one problem is that HBA does not adopt computational complexity of the used data mining technique. This is due to the way objective function is defined. So, in this paper, we have proposed an improved HBA (IHBA), which is utilising a modified objective function that compute the computational complexity of the used classification method. We also test several clustering techniques as HBA parameters tuning, in order to enhance classifiers' performance. We have tested IHBA on different benchmarks and the obtained results show the effectiveness of the proposed method.