An Evolutionary Classifier Based on Adaptive Resonance Theory Network II and Genetic Algorithm

Adaptive resonance theory network II (ART2) is a neural network concerning unsupervised learning. It has been shown that ART2 is suitable for clustering problems that require on-line learning of large-scale and evolving databases. However, if applied to classification problems, ART2 suffers from deficiencies in terms of interpretation of class labels and sensitivity to the input data-order. This study proposes a novel evolutionary classifier based on adaptive resonance theory network II and genetic algorithms. In the proposed classifier, ART2 is used first for generating the weights between attributes and clusters. In the second stage, a genetic algorithm is employed to generate class labels of input data. The performance of the proposed algorithm is evaluated using Hayes datasets from the machine learning repository at UCI. The experimental results show that the proposed classifier is as good as the well-known C5.0 classifier in terms of accuracy.

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