A Comparison Of Bio-inspired Metaheuristic
Approaches In Classification Tasks
Author(s)
R. L. Oliveira, B. S. L. P. de Lima & N. F. F. Ebecken
Abstract
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
Keywords
data mining, bio-inspired metaheuristics, Genetic Algorithms, Ant
colony optimization and Artificial Immune Systems.
Related Book
Other papers in this volume
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