On the harmonious mating strategy through tabu search
Information Sciences: an International Journal - Special issue: Evolutionary computation
On Visualization and Aggregation of Nearest Neighbor Classifiers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Some Symmetry Based Classifiers
Fundamenta Informaticae
A Method to Classify Data by Fuzzy Rule Extraction from Imbalanced Datasets
Proceedings of the 2006 conference on Artificial Intelligence Research and Development
A multidimensional hybrid intelligent method for gear fault diagnosis
Expert Systems with Applications: An International Journal
Fuzzy rule extraction using recombined RecBF for very-imbalanced datasets
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
A coevolutionary approach to optimize class boundaries for multidimensional classification problems
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part IV
SoHyFIS-Yager: A self-organizing Yager based Hybrid neural Fuzzy Inference System
Expert Systems with Applications: An International Journal
Some Symmetry Based Classifiers
Fundamenta Informaticae
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A method is described for finding decision boundaries, approximated by piecewise linear segments, for classifying patterns in ℜN,N⩾2, using an elitist model of genetic algorithms. It involves generation and placement of a set of hyperplanes (represented by strings) in the feature space that yields minimum misclassification. A scheme for the automatic deletion of redundant hyperplanes is also developed in case the algorithm starts with an initial conservative estimate of the number of hyperplanes required for modeling the decision boundary. The effectiveness of the classification methodology, along with the generalization ability of the decision boundary, is demonstrated for different parameter values on both artificial data and real life data sets having nonlinear/overlapping class boundaries. Results are compared extensively with those of the Bayes classifier, k-NN rule and multilayer perceptron