C4.5: programs for machine learning
C4.5: programs for machine learning
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Feature selection for high-dimensional genomic microarray data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Consistency Based Feature Selection
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Chi2: Feature Selection and Discretization of Numeric Attributes
TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
An introduction to variable and feature selection
The Journal of Machine Learning Research
Redundancy based feature selection for microarray data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Projection-based measure for efficient feature selection
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - IBERAMIA '02
Filter versus wrapper gene selection approaches in DNA microarray domains
Artificial Intelligence in Medicine
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Traditional gene selection methods often select the top–ranked genes according to their individual discriminative power. We propose to apply feature evaluation measure broadly used in the machine learning field and not so popular in the DNA microarray field. Besides, the application of sequential gene subset selection approaches is included. In our study, we propose some well-known criteria (filters and wrappers) to rank attributes, and a greedy search procedure combined with three subset evaluation measures. Two completely different machine learning classifiers are applied to perform the class prediction. The comparison is performed on two well–known DNA microarray data sets. We notice that most of the top-ranked genes appear in the list of relevant–informative genes detected by previous studies over these data sets.