Fast learning artificial neural network (FLANN II) using the nearest neighbour recall
Neural, Parallel & Scientific Computations
Combining Nearest Neighbor Classifiers Through Multiple Feature Subsets
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Clustering for bioinformatics via matrix optimization
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
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The K-means Fast Learning Artificial Neural Network (KFLANN) is a small neural network bearing two types of parameters, the tolerance, δ and the vigilance, μ In previous papers, it was shown that the KFLANN was capable of fast and accurate assimilation of data [12] However, it was still an unsolved issue to determine the suitable values for δ and μ in [12] This paper continues to follows-up by introducing Genetic Algorithms as a possible solution for searching through the parameter space to effectively and efficiently extract suitable values to δ and μ It is also able to determine significant factors that help achieve accurate clustering Experimental results are presented to illustrate the hybrid GA-KFLANN ability using available test data.