Meta-heuristic Algorithm As Feature Selector For Convolutional Neural Networks

Dawid Polap, Marcin Wozniak, Jacek Mandziuk

Abstract: The huge popularity of heuristics contributes not only to the improvement and modeling of new solutions but also to their adaptation to selected goals. Recent years have shown the popularity of their use also in machine learning as a training algorithm or allowing for the selection of optimal architecture or hyper-parameters. In this paper, we propose an adaptation of a nature-inspired algorithm for preprocessing images in a parallel way for obtaining higher classification results. The proposed idea is based on analyzing images by heuristic representative which is Red Fox Optimization Algorithm and returning a specific value. These values are used in deciding to classify the entire image or trim it to eliminate unnecessary objects. We modeled this solution and evaluated using the learning transfer method for VOC 2007 dataset. The obtained results were compared on selected classes to show the advantages of a proposal.