Skip to main content

IHBA: An Improved Homogeneity-Based Algorithm for Data Classification

  • Conference paper

Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT,volume 456)


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.


  • Metaheuristics
  • HBA
  • Improvement
  • Machine Learning
  • Medical Informatics


  1. Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1992, 1975) (re-issued by MIT Press)

    Google Scholar 

  2. Kirkpatrick, S., Gelatt Jr., C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220, 671–680 (1983)

    CrossRef  MATH  MathSciNet  Google Scholar 

  3. Talbi, E.-G.: Metaheuristics: From Design to Implementation. Wiley (June 2009)

    Google Scholar 

  4. Pham, H.N.A., Triantaphyllou, E.: The impact of overfitting and overgeneralization on the classification accuracy in data mining. In: Maimon, O., Rokach, L. (eds.) Soft Computing for Knowledge Discovery and Data Mining, part 4, ch. 5, pp. 391–431. Springer, New York (2007)

    Google Scholar 

  5. Pham, H.N.A., Triantaphyllou, E.: Prediction of diabetes by employing a new data mining approach which balances fitting and generalization. In: Lee, R., Kim, H.-K. (eds.) Computer and Information Science. SCI, vol. 131, pp. 11–26. Springer, Heidelberg (2008)

    CrossRef  Google Scholar 

  6. Pham, H.N.A., Triantaphyllou, E.: An application of a new meta-heuristic for optimizing the classification accuracy when analyzing some medical datasets. Expert Systems with Applications 36(5), 9240–9249 (2009)

    CrossRef  Google Scholar 

  7. Carvalho, A.R., Ramos, F.M., Chaves, A.A.: Metaheuristics for the feedforward artificial neural network (ANN) architecture optimization problem. Neural Computing and Applications 20(8), 1273–1284 (2011)

    CrossRef  Google Scholar 

  8. Weiss, S.M., Kapouleas, I.: An empirical comparison of pattern recognition, neural nets and machine learning classification methods. In: Shavlik, J.W., Dietterich, T.G. (eds.) Readings in Machine Learning. Morgan Kauffman Publ., CA (1990)

    Google Scholar 

  9. UCI repository of machine learning databases, University of California at Irvine, Departmentof Computer Science, (last accessed 2015)

  10. Jang, J.S.R.: Anfis: adaptative network-based fuzzy inference système. IEEE Trans. on Systems, Man and Cybernetics (1993)

    Google Scholar 

  11. Kohonen, T.: The Self-Organizing Map. Proceedings of the IEEE 78(9), 1464–1480 (1990)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Fatima Bekaddour .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2015 IFIP International Federation for Information Processing

About this paper

Cite this paper

Bekaddour, F., Amine, C.M. (2015). IHBA: An Improved Homogeneity-Based Algorithm for Data Classification. In: Amine, A., Bellatreche, L., Elberrichi, Z., Neuhold, E., Wrembel, R. (eds) Computer Science and Its Applications. CIIA 2015. IFIP Advances in Information and Communication Technology, vol 456. Springer, Cham.

Download citation

  • DOI:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19577-3

  • Online ISBN: 978-3-319-19578-0

  • eBook Packages: Computer ScienceComputer Science (R0)