Title: An improved HBA metaheuristic

Authors: Fatima Bekaddour; Mohammed Amine Chikh

Addresses: Abdelhamid Mehri – Constantine University, Fbourg 54, Imama, Tlemcen, Algérie ' Abou Bekr Belkaid – Tlemcen University, BP 230 Pôle Chetouane, 13000 Tlemcen, Algérie

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.

Keywords: metaheuristics; performance; homogeneity-based algorithm; HBA; optimisation; data mining.

DOI: 10.1504/IJRIS.2017.086144

International Journal of Reasoning-based Intelligent Systems, 2017 Vol.9 No.1, pp.12 - 21

Accepted: 18 Dec 2016
Published online: 27 Aug 2017 *

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