A Comparison Of Bio-inspired Metaheuristic Approaches In Classification Tasks
Free (open access)
R. L. Oliveira, B. S. L. P. de Lima & N. F. F. Ebecken
This paper presents a comparative analysis of three computational tools based in metaheuristics inspired by nature to perform an important data mining task. These tools are employed to generate classification rules from databases. The first one uses the Ant Colony metaphor that is one of the most recent nature-inspired metaheuristics. The second one employs the Artificial Immune System paradigm that is also a relatively new biologically-inspired paradigm. The third one employs a fuzzy genetic approach. The main motivation for applying those heuristics to data mining is that bio-inspired algorithms have shown to be robust search methods. In this work, basic concepts of the employed strategies are presented and significant aspects related to each approach are discussed. Some data sets from the UCI repository were employed to evaluate the performance of the tools. The comparative survey of the classification tasks is performed emphasizing the importance of discovering comprehensible and accurate knowledge. Keywords: data mining, bio-inspired metaheuristics, Genetic Algorithms, Ant colony optimization and Artificial Immune Systems. 1 Introduction Researchers of several areas have observed that various principles and theories about nature and the subsequent development of models, based in these systems, have been implemented using computers systems with great potential to solve complex problems. New strategies have been developed, inspired in biological or
data mining, bio-inspired metaheuristics, Genetic Algorithms, Ant colony optimization and Artificial Immune Systems.