Title: Biogeography-based optimisation for data classification problems

Authors: Mohammed Alweshah; Abdelaziz I. Hammouri; Sara Tedmori

Addresses: Department of Computer Science, Prince Abdullah Bin Ghazi Faculty of Information Technology, Al-Balqa' Applied University, Al-Salt, Jordan ' Department of Computer Science, Prince Abdullah Bin Ghazi Faculty of Information Technology, Al-Balqa' Applied University, Al-Salt, Jordan ' Department of Computer Science, King Hussein Faculty of Computing Sciences, Princess Sumaya University for Technology, Amman, Jordan

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.

Keywords: biogeography-based optimisation; probabilistic neural networks; PNNs; classification problem.

DOI: 10.1504/IJDMMM.2017.085645

International Journal of Data Mining, Modelling and Management, 2017 Vol.9 No.2, pp.142 - 162

Received: 05 May 2016
Accepted: 11 Sep 2016

Published online: 28 Jul 2017 *

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