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Published in:
2015 | OriginalPaper | Chapter
A Honey Bee Mating Optimization Based Gradient Descent Learning – FLANN (HBMO-GDL-FLANN) for Classification
Authors : Bighnaraj Naik, Janmenjoy Nayak, H. S. Behera
Published in: Emerging ICT for Bridging the Future - Proceedings of the 49th Annual Convention of the Computer Society of India CSI Volume 2
Publisher: Springer International Publishing
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Motivated from successful use of the Honey Bee Mating Optimization (HMBO) in many applications, in this paper, a HMBO based Gradient Descent Learning for FLANN classifier is proposed and compared with FLANN, GA based FLANN and PSO based FLANN classifiers. The proposed method mimics the iterative mating process of honey bees and strategies to select eligible drones for mating process, for selection of best weights for FLANN classifiers. The classification accuracy of HMBO-GDL-FLANN is investigated and compared with FLANN, GA-based FLANN and PSO-based FLANN. These models have been implemented using MATLAB and results are statistically analyzed under one way ANOVA test. To obtain generalized performance, the proposed method has been tested under 5-fold cross validation.