Abstract
Microarray data analysis is attracting increasing attention in computer science because of the many applications of machine learning methods in prediction 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 prediction accuracy reached show the feasibility of the hybrid model proposed here and indicate an interesting research line for the near future.
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Couce, Y., Franco, L., Urda, D., Subirats, J.L., Jerez, J.M. (2011). Hybrid (Generalization-Correlation) Method for Feature Selection in High Dimensional DNA Microarray Prediction Problems. In: Cabestany, J., Rojas, I., Joya, G. (eds) Advances in Computational Intelligence. IWANN 2011. Lecture Notes in Computer Science, vol 6692. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21498-1_26
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DOI: https://doi.org/10.1007/978-3-642-21498-1_26
Publisher Name: Springer, Berlin, Heidelberg
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