Learning automata based classifier
Pattern Recognition Letters
A learning automata based algorithm for optimization of continuous complex functions
Information Sciences: an International Journal
Bayesian and Decision Tree Approaches for Pattern Recognition Including Feature Measurement Costs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Varieties of learning automata: an overview
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Among the various traditional approaches of pattern recognition, the statistical approach has been most intensively studied and used in practice. This paper presents a new classifier called MLAC for multiclass classification based on the learning automata. The proposed classifier using a soft decision method could find the optimal hyperplanes in solution space and separate available classes from each other well. We have tested the MLAC classifier on some multiclass datasets including IRIS, WINE and GLASS. The results show a significant improvement in comparison with the previous learning automata based classifiers as it has more accuracy and lower running time. Also, in order to evaluate performance of the proposed MLAC classifier, it has been compared with conventional classifiers such as K-Nearest Neighbor, Multilayer Perceptron, Genetic classifier and Particle Swarm classifier on these datasets in terms of accuracy. The obtained results show that the proposed MLAC classifier not only improves the classification's accuracy, but also reduces time complexity.