Solving nonlinear classification problems with black hole optimisation and higher order Jordan Pi-sigma neural network: a novel approach Online publication date: Fri, 06-Jan-2017
by Janmenjoy Nayak; Bighnaraj Naik; Himansu Sekhar Behera
International Journal of Computational Systems Engineering (IJCSYSE), Vol. 2, No. 4, 2016
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.
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