Paper Title
Using Feature Selection Algorithms to Design for Unsupervised Image Classification Based on the ABC Algorithm

Abstract - This chapter examines the application of the artificial Bee Colony (ABC) set of rules for computing pixel classification for image segmenting. Classification of data in big repositories need efficient analytic techniques, as a huge number of features are generated to improve the representation of such images. During the feature selection process, optimization methods can be used to find the most important subset of features from the data set while keeping the accuracy rate of the original set of features. Unsupervised classification is a way of processing images that is based on putting pixels together into groups or themes. This study looks into, implements, and analyses a feature for selecting features that is based on the Artificial Bee Colony. This method is straightforward and flexible in comparison to different bio-stimulated algorithms, and it has the blessings of rapid convergence and much less reminiscence. The ABC classification is used to sort different kinds of data. Keywords - Artificial Bee Colony, Image segmentation, Optimization, Classification.