Data feature selection based on Artificial Bee Colony algorithm

Data analysis aims at extracting and modeling information content to identify patterns within the data. As a manner of simplifying the amount of information to describe a large set of data, features are extracted from the data, serving as representative characteristics of its contents. In image analysis, for instance, examples of features include color, texture, edges, object shape, interest points, among others. These features usually are organized into an n-dimensional feature vector. Feature selection is an important step used in several tasks, such as image classification, cluster analysis, data mining, pattern recognition, image retrieval, among others. It is a crucial preprocessing technique for effective data analysis, where only a subset from the original data features is chosen to eliminate noisy, irrelevant or redundant features.