Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
An introduction to variable and feature selection
The Journal of Machine Learning Research
ACM SIGKDD Explorations Newsletter
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A review of feature selection techniques in bioinformatics
Bioinformatics
A hybrid GA/SVM approach for gene selection and classification of microarray data
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
Meta-Lamarckian learning in memetic algorithms
IEEE Transactions on Evolutionary Computation
Multi-objective genetic algorithm evaluation in feature selection
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
A hybrid model to favor the selection of high quality features in high dimensional domains
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Assessing similarity of feature selection techniques in high-dimensional domains
Pattern Recognition Letters
Hybrid feature selection through feature clustering for microarray gene expression data
International Journal of Hybrid Intelligent Systems
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The classification of cancers from gene expression profiles is a challenging research area in bioinformatics since the high dimensionality of microarray data results in irrelevant and redundant information that affects the performance of classification. This paper proposes using an evolutionary algorithm to select relevant gene subsets in order to further use them for the classification task. This is achieved by combining valuable results from different feature ranking methods into feature pools whose dimensionality is reduced by a wrapper approach involving a genetic algorithm and SVM classifier. Specifically, the GA explores the space defined by each feature pool looking for solutions that balance the size of the feature subsets and their classification accuracy. Experiments demonstrate that the proposed method provide good results in comparison to different state of art methods for the classification of microarray data.