A dynamic wrapper method for feature discretization and selection
Figueiredo, M. A. T.
A dynamic wrapper method for feature discretization and selection, Proc International Conf. on Pattern Recognition Applications and Methods - ICPRAM, vilamoura, Portugal, Vol. , pp. 103 - 112, February, 2012.
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In many learning problems, an adequate (sometimes discrete) representation of the data is necessary. For instance, for large number of features and small number of instances, learning algorithms may be confronted with the curse of dimensionality, and need to address it in order to be effective. Feature selection and feature discretization techniques have been used to achieve adequate representations of the data, by selecting an adequate subset of features with a convenient representation. In this paper, we propose static and dynamic methods for feature discretization. The static method is unsupervised and the dynamic method uses a wrapper approach with a quantizer and a classifier, and it can be coupled with any static (unsupervised or supervised) discretization procedure. The proposed methods attain efficient representations that are suitable for learning problems. Moreover, using well-known feature selection methods with the features discretized by our methods leads to better accuracy than with the features discretized by other methods or even with the original features.