A global optimum approach for one-layer neural networks
Neural Computation
Proportional k-Interval Discretization for Naive-Bayes Classifiers
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Consistency-based search in feature selection
Artificial Intelligence
A review of feature selection techniques in bioinformatics
Bioinformatics
Searching for interacting features
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Gene selection from microarray data for cancer classification-a machine learning approach
Computational Biology and Chemistry
A new supervised local modelling classifier based on information theory
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
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Gene-expression microarray is a novel technology that allows to examine tens of thousands of genes at a time. For this reason, manual observation is not feasible anymore and machine learning methods are progressing to analyze these new data. Specifically, since the number of genes is very high, feature selection methods have proven valuable to deal with this unbalanced - high dimensionality and low cardinality - datasets. Our method is composed by a discretizer, a filter and the FVQIT (Frontier Vector Quantization using Information Theory) classifier. It is employed to classify eight DNA gene-expression microarray datasets of different kinds of cancer. A comparative study with other classifiers such as Support Vector Machine (SVM), C4.5, naïve Bayes and k-Nearest Neighbor is performed. Our approach shows excellent results outperforming all other classifiers.