TITLE:
Evolutionary Learning of Concepts
AUTHORS:
Rodrigo Morgon, Silvio do Lago Pereira
KEYWORDS:
Evolutionary Algorithms, Machine Learning, Classification, Interaction, Imbalance, Noise
JOURNAL NAME:
Journal of Computer and Communications,
Vol.2 No.8,
June
27,
2014
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.