Machine Learning
Feature Extraction, Construction and Selection: A Data Mining Perspective
Feature Extraction, Construction and Selection: A Data Mining Perspective
Multi-class ROC analysis from a multi-objective optimisation perspective
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A Branch and Bound Algorithm for Feature Subset Selection
IEEE Transactions on Computers
Monte Carlo feature selection for supervised classification
Bioinformatics
A genetic embedded approach for gene selection and classification of microarray data
EvoBIO'07 Proceedings of the 5th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
ParadisEO-MOEO: a framework for evolutionary multi-objective optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
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
Evolutionary Rough Feature Selection in Gene Expression Data
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
A case-based knowledge system for safety evaluation decision making of thermal power plants
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Bi-objective feature selection for discriminant analysis in two-class classification
Knowledge-Based Systems
Gene expression rule discovery and multi-objective ROC analysis using a neural-genetic hybrid
International Journal of Data Mining and Bioinformatics
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The study of the sensitivity and the specificity of a classification test constitute a powerful kind of analysis since it provides specialists with very detailed information useful for cancer diagnosis. In this work, we propose the use of a multiobjective genetic algorithm for gene selection of Microarray datasets. This algorithm performs gene selection from the point of view of the sensitivity and the specificity, both used as quality indicators of the classification test applied to the previously selected genes. In this algorithm, the classification task is accomplished by Support Vector Machines; in addition a 10-Fold Cross-Validation is applied to the resulting subsets. The emerging behavior of all these techniques used together is noticeable, since this approach is able to offer, in an original and easy way, a wide range of accurate solutions to professionals in this area. The effectiveness of this approach is proved on public cancer datasets by working out new and promising results. A comparative analysis of our approach using two and three objectives, and with other existing algorithms, suggest that our proposal is highly appropriate for solving this problem.