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Labatut, V. and Cherifi, H. (2012) Accuracy Measures for the Comparison of Classifiers. Proceedings of the 5th International Conference on Information Technology, Chania Crete, 7-9 July 2014, 1-5. http://arxiv.org/ftp/arxiv/papers/1207/1207.3790.pdf

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• 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.