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Biogeography-based optimisation for data classification problems

Author

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  • Mohammed Alweshah
  • Abdelaziz I. Hammouri
  • Sara Tedmori

Abstract

Classification is a task of supervised learning whose aim is to identify to which of a set of categories a new input element belongs. Probabilistic neural network is a variant of artificial neural network, which is simple in structure, easy for training and often used in classification problems. In this paper, the authors propose an improved probabilistic neural network model that employs biogeography-based optimisation to enhance the accuracy of the classification. The proposed approach was tested on 11 standard benchmark medical datasets from the machine-learning repository. Results show that the classification accuracy of the proposed improved probabilistic neural network model outperforms that of the traditional probabilistic neural network model.

Suggested Citation

  • Mohammed Alweshah & Abdelaziz I. Hammouri & Sara Tedmori, 2017. "Biogeography-based optimisation for data classification problems," International Journal of Data Mining, Modelling and Management, Inderscience Enterprises Ltd, vol. 9(2), pages 142-162.
    • Handle: RePEc:ids:ijdmmm:v:9:y:2017:i:2:p:142-162
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    Cited by:

    1. Parimal Kumar Giri & Sagar S. De & Sachidananda Dehuri & Sung‐Bae Cho, 2021. "Biogeography based optimization for mining rules to assess credit risk," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 28(1), pages 35-51, January.

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