Enhanced hierarchical fuzzy model using evolutionary GA with modified ABC algorithm for classification problem

Abstract

This paper enhances the hierarchical fuzzy model to deal with the classification problems by adopting evolutionary genetic algorithm (GA) with a modified artificial bee colony (ABC) algorithm. Traditionally, fuzzy classifier could not provide a sufficiently high classification rate in higher feature dimension with few rules. In the literature, the genetic algorithm can take advantage from the global searching; moreover, the characteristic of ABC can enhance the local searching. Therefore, the hierarchical fuzzy model integrates GA with a modified ABC algorithm is constructed in this study to recognize some classification problems. The classification simulation includes three benchmark databases such as Glass, Wine, and Iris database. The result demonstrates that using evolutionary GA and modified ABC algorithm is beneficial than that without turning. Therefore, it is clearly that our methodology considers not only the global exploration but also the local exploitation.

Advanced Hierarchical Fuzzy Classification Model Adopting Symbiosis Based DNA-ABC Optimization Algorithm

“…Several kinds of population-based algorithms have been utilized for scientific computation. For instance, the fuzzy classification system is improved through ant colony optimization (ACO) based local searcher [13], feature selection for parameter optimization by using the methodology of fuzzy criterion (FOF) with ACO algorithm [14], image registration optimization solved by firefly algorithm (FA) [15], data clustering improved by shuffled frog-leaping algorithm [16], multi-objective numerical problems solved by the artificial bee colony (ABC) algorithm [17], and [18] adopting GA and ABC to enhance the fuzzy input feature selection.…”