Title: Solving nonlinear classification problems with black hole optimisation and higher order Jordan Pi-sigma neural network: a novel approach

Authors: Janmenjoy Nayak; Bighnaraj Naik; Himansu Sekhar Behera

Addresses: DST Inspire Fellow, Government of India, New Delhi, India; Department of CSE, Modern Engineering and Management Studies, Balasore, Odisha-756056, India ' Department of Computer Application, Veer Surendra Sai University of Technology (formerly U.C.E., Burla), Burla – 768018, Odisha, India ' Department of Computer Science Engineering and Information Technology, Veer Surendra Sai University of Technology (formerly U.C.E., Burla), Burla – 768018, Odisha, India

Abstract: Nature inspired optimisation algorithms have been on a leading focus of all researchers since last few decades. Successful applications of such algorithms for solving diversified problem domains make them more popular. This presented research is about to implement a novel nature inspired algorithm with higher order neural network for solving nonlinear classification problems of data mining. A recently developed nature inspired algorithm, black hole optimisation has been used to train the weights of a higher order Jordan Pi-sigma neural network and to optimise the performance of the network. The proposed black hole algorithm-based higher order neural network has been developed for solving the classification problems in data mining. The result of the proposed method is compared with some other benchmark meta-heuristic techniques like PSO, GA and found that the proposed method is superior to other approaches.

Keywords: black hole optimisation; BHO; Jordan Pi-sigma neural networks; JPSNN; classification; nature inspired optimisation; higher order neural networks; PSO; particle swarm optimisation; GAs; genetic algorithms; nonlinear classification; data mining; metaheuristics.

DOI: 10.1504/IJCSYSE.2016.081392

International Journal of Computational Systems Engineering, 2016 Vol.2 No.4, pp.236 - 251

Received: 22 Mar 2016
Accepted: 14 May 2016

Published online: 06 Jan 2017 *

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