Digging into acceptor splice site prediction: an iterative feature selection approach
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Information preserving multi-objective feature selection for unsupervised learning
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Evolutionary continuation methods for optimization problems
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Hybridization of Evolutionary Mechanisms for Feature Subset Selection in Unsupervised Learning
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
Using typical testors for feature selection in text categorization
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
A fast implementation of the CT_EXT algorithm for the testor property identification
MICAI'10 Proceedings of the 9th Mexican international conference on Artificial intelligence conference on Advances in soft computing: Part II
Evolutionary computation in the identification of risk factors. Case of TRALI
Expert Systems with Applications: An International Journal
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Typical testors are useful for both feature selection and feature relevance determination in supervised classification problems. However, reported algorithms that address the problem of finding the set of all typical testors have exponential complexity. In this paper, we propose to adapt an evolutionary method, the Hill-Climbing algorithm, with an acceleration operator in mutation process, to address this problem in polinomial time. Experimental results with the method proposed are presented and compared, in efficiency, with other methods, namely, Genetic Algorithms (GA) and Univariate Marginal Distribution Algorithm (UMDA).