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Evolutionary Learning of Concepts

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http://www.scirp.org/journal/PaperInformation.aspx?PaperID=47412#.VMib_izQrzE

Author(s) 
Rodrigo Morgon, Silvio do Lago Pereira

Affiliation(s)
Department of Information Technology, FATEC-SP/CEETEPS, S?o Paulo, Brazil.
Department of Information Technology, FATEC-SP/CEETEPS, S?o Paulo, Brazil.

ABSTRACT
Concept learning is a kind of classification task that has interesting practical applications in several areas. In this paper, a new evolutionary concept learning algorithm is proposed and a corresponding learning system, called ECL (Evolutionary Concept Learner), is implemented. This system is compared to three traditional learning systems: MLP (Multilayer Perceptron), ID3 (Iterative Dichotomiser) and NB (Naïve Bayes). The comparison takes into account target concepts of varying complexities (e.g., with interacting attributes) and different qualities of training sets (e.g., with imbalanced classes and noisy class labels). The comparison results show that, although no single system is the best in all situations, the proposed system ECL has a very good overall performance.

KEYWORDS
Evolutionary Algorithms, Machine Learning, Classification, Interaction, Imbalance, Noise

Cite this paper
Morgon, R. and Pereira, S. (2014) Evolutionary Learning of Concepts. Journal of Computer and Communications, 2, 76-86. doi: 10.4236/jcc.2014.28008.

References
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