Using Genetic Algorithms for Concept Learning
Machine Learning - Special issue on genetic algorithms
A Knowledge-Intensive Genetic Algorithm for Supervised Learning
Machine Learning - Special issue on genetic algorithms
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
SIA: A Supervised Inductive Algorithm with Genetic Search for Learning Attributes based Concepts
ECML '93 Proceedings of the European Conference on Machine Learning
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Chi2: Feature Selection and Discretization of Numeric Attributes
TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
Theoretical and Empirical Analysis of ReliefF and RReliefF
Machine Learning
Benchmarking Attribute Selection Techniques for Discrete Class Data Mining
IEEE Transactions on Knowledge and Data Engineering
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Natural coding: a more efficient representation for evolutionary learning
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Evolutionary learning of hierarchical decision rules
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolutionary concept learning in first order logic: an overview
AI Communications
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This paper presents an approach that deals with the feature selection problem, and includes two main aspects: first, the selection is done during the evolutionary learning process, i.e., it is a dynamic approach; and second, the selection is local, i.e., the algorithm selects the best features from the best space region to learn at a given time of the exploration process. While the traditional feature selection is based on the attribute relevance, our approach is based on a new concept, called feature influence, which is aware of the dynamics and locality of the concept. The feature influence provides a measure of the attribute relevance at a certain instant of the evolutionary learning process, since it depends on each generation. Experimental results have been obtained by comparing an EA--based supervised learning algorithm to its modified version to include the concept approached. The results show an excellent performance, as the new adapted algorithm achieves the same classification results while using less rules, less conditions in rules and much less generations. The experiments include the statistical significance of the improvement over a set of sixteen datasets from the UCI repository.