An efficient FLANN model with CRO-based gradient descent learning for classification
by Bighnaraj Naik; Janmenjoy Nayak; Himansu Sekhar Behera
International Journal of Business Information Systems (IJBIS), Vol. 21, No. 1, 2016

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.

Online publication date: Tue, 01-Dec-2015

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Business Information Systems (IJBIS):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com