Abstract
In classification and clustering problems, feature selection techniques can be used to reduce the dimensionality of the data and increase the performances. However, feature selection is a challenging task, especially when hundred or thousands of features are involved. In this framework, we present a new approach for improving the performance of a filter-based genetic algorithm. The proposed approach consists of two steps: first, the available features are ranked according to a univariate evaluation function; then the search space represented by the first M features in the ranking is searched using a filter-based genetic algorithm for finding feature subsets with a high discriminative power.
Experimental results demonstrated the effectiveness of our approach in dealing with high dimensional data, both in terms of recognition rate and feature number reduction.
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Notes
- 1.
Note that the same holds also for the feature-class correlation.
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De Stefano, C., Fontanella, F., Scotto di Freca, A. (2017). Feature Selection in High Dimensional Data by a Filter-Based Genetic Algorithm. In: Squillero, G., Sim, K. (eds) Applications of Evolutionary Computation. EvoApplications 2017. Lecture Notes in Computer Science(), vol 10199. Springer, Cham. https://doi.org/10.1007/978-3-319-55849-3_33
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