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Hybrid (generalization-correlation) method for feature selection in high dimensional DNA microarray pbkp_rediction problems

Abstract:

Microarray data analysis is attracting increasing attention in computer science because of the many applications of machine learning methods in pbkp_rediction problems. The process typically involves a feature selection step, important in order to increase the accuracy and speed of the classifiers. This work analyzes the characteristics of the features selected by two wrapper methods, the first one based on artificial neural networks (ANN) and the second in a novel constructive neural network (CNN) algorithm, to later propose a hybrid model that combines the advantages of wrapper and filter methods. The results obtained in terms of the computational costs involved and the pbkp_rediction accuracy reached show the feasibility of the hybrid model proposed here and indicate an interesting research line for the near future. © 2011 Springer-Verlag.