Fundamentals of algorithmics
Learning from Examples with Information Theoretic Criteria
Journal of VLSI Signal Processing Systems
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
Chi2: Feature Selection and Discretization of Numeric Attributes
TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
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
Combining Feature Selection and Local Modelling in the KDD Cup 99 Dataset
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
ChiMerge: discretization of numeric attributes
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Local modeling classifier for microarray gene-expression data
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Data Mining: Practical Machine Learning Tools and Techniques
Data Mining: Practical Machine Learning Tools and Techniques
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Gene-expression microarray is a novel technology that allows the examination of tens of thousands of genes at a time. For this reason, manual observation is not feasible and machine learning methods are progressing to face these new data. Specifically, since the number of genes is very high, feature selection methods have proven valuable to deal with these unbalanced-high dimensionality and low cardinality-data sets. In this work, the FVQIT (Frontier Vector Quantization using Information Theory) classifier is employed to classify twelve DNA gene-expression microarray data sets of different kinds of cancer. A comparative study with other well-known classifiers is performed. The proposed approach shows competitive results outperforming all other classifiers.