M. Richeldi and P. L. Lanzi. Improving genetic-based feature selection by reducing data dimensionality. In Proc. of the Workshop on Evolutionary Computation, Int. Conf. on Machine Learning, 1996. 21 Home/Search Document Details and Download
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Feature Selection - Portinale, Saitta (2002) (Correct)
....step. As a consequence, feature subsets that reflect all the problem dimensions are formed. 15 We investigated several search heuristics to select the smallest number of features from each factor [50] Among the others, genetic algorithms (GAs) turned out to be an excellent fit to this task [47]. In our experiments, fitness associated to a feature subset x was the ten fold crossvalidated predictive accuracy of the C4.5 induction algorithm that would learn the data characterized by the x features only. The size of the space originated by factors turned out to be one order of magnitude ....
M. Richeldi and P. L. Lanzi. Improving genetic-based feature selection by reducing data dimensionality. In Proc. of the Workshop on Evolutionary Computation, Int. Conf. on Machine Learning, 1996. 21
ADHOC: a Tool for Performing Effective Feature Selection - Richeldi, Lanzi (1996) (1 citation) Self-citation (Richeldi Lanzi) (Correct)
....reduction step. As a consequence, feature subsets that reflect all the problem dimensions are formed. We investigated several search heuristics to select the smallest number of features from each factor [10] Among the others, genetic algorithms (GAs) turned out to be an excellent fit to this task [11]. In our experiments, fitness associated to a feature subset x was the ten fold crossvalidated predictive accuracy of the C4.5 induction algorithm [6] that would learn the data characterized by the x features only. The size of the space originated by factors turned out to be one order of magnitude ....
M. Richeldi and P. L. Lanzi. Improving Genetic-Based Feature Selection by Reducing Data Dimensionality. Proc. of the Workshop on Evolutionary Computation, Int. Conf. on Machine Learning, 1996.
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