Title: An efficient FLANN model with CRO-based gradient descent learning for classification

Authors: Bighnaraj Naik; Janmenjoy Nayak; Himansu Sekhar Behera

Addresses: Department of Computer Science Engineering and Information Technology, Veer Surendra Sai University of Technology, Burla – 768018, Odisha, India ' Department of Computer Science Engineering and Information Technology, Veer Surendra Sai University of Technology, Burla – 768018, Odisha, India ' Department of Computer Science Engineering and Information Technology, Veer Surendra Sai University of Technology, Burla – 768018, Odisha, India

Abstract: Due to the nonlinear nature of real world data, it is difficult to determine the optimal ANN classification model with accurate and fast convergence. Although, many higher order ANN have been designed and integrated with competitive optimisation method in order to construct an accurate classification model, but the parameter adjustment and variability in performance in different runs of the classification model leads to statistically insignificant result. In this paper, a FLANN model (CRO-GDL-FLANN) has been proposed for classification with gradient descent learning (GDL) based on chemical reaction optimisation (CRO). The proposed CRO-GDL-FLANN method has been tested with various benchmark datasets from the UCI machine learning repository under five fold cross-validations. The classification accuracy of CRO-GDL-FLANN is compared with FLANN, GA-FLANN and PSO-FLANN. To prove the proposed method is statistically better and significantly different from other alternatives, the CRO-GDL-FLANN is verified under multiple comparisons of classifiers by using Friedman, Tukey and Dunnett statistical test. Finally, one-way-ANOVA test has been carried out for generalised comparison of CRO-GDL-FLANN with other classifiers.

Keywords: data mining; machine learning; classification accuracy; functional link ANNs; artificial neural networks; FLANN; gradient descent learning; GDL; chemical reaction optimisation; CRO; particle swarm optimisation; PSO; genetic algorithms; ANOVA; classifiers.

DOI: 10.1504/IJBIS.2016.073381

International Journal of Business Information Systems, 2016 Vol.21 No.1, pp.73 - 116

Received: 24 Feb 2014
Accepted: 03 Sep 2014
Published online: 01 Dec 2015 *

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