Unknown attribute values in induction
Proceedings of the sixth international workshop on Machine learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
SOAP: Efficient Feature Selection of Numeric Attributes
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
Evaluating Feature Selection Methods for Learning in Data Mining Applications
HICSS '98 Proceedings of the Thirty-First Annual Hawaii International Conference on System Sciences-Volume 5 - Volume 5
Feature Selection Algorithms: A Survey and Experimental Evaluation
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
An introduction to variable and feature selection
The Journal of Machine Learning Research
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
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We present two feature selection methods, inspired in the Shannon's entropy and the Information Gain measures, that are easy to implement. These methods apply when we have a database with continuous attributes and discrete multi- class. The first method applies when attributes are independent among them given the class. The second method is useful when we suspect that interdependencies among the attributes exist. In the experiments that we realized, with synthetic and real databases, the proposed methods are shown to be fast and to produce near optimum solutions, with a good feature reduction ratio.