IHBA: An Improved Homogeneity-Based Algorithm for Data Classification - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2015

IHBA: An Improved Homogeneity-Based Algorithm for Data Classification


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.
Fichier principal
Vignette du fichier
339159_1_En_11_Chapter.pdf (643.96 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01789975 , version 1 (11-05-2018)





Fatima Bekaddour, Chikh Mohammed Amine. IHBA: An Improved Homogeneity-Based Algorithm for Data Classification. 5th International Conference on Computer Science and Its Applications (CIIA), May 2015, Saida, Algeria. pp.129-140, ⟨10.1007/978-3-319-19578-0_11⟩. ⟨hal-01789975⟩
68 View
140 Download



Gmail Facebook X LinkedIn More