Abstract
All learning algorithms perform very well when provided with a small number of highly relevant features. This paper proposes a constructive induction method to automatically construct such features. The method, named GLOREF (GLObally RElevant Features), exploits low-level interactions between the attributes in order to generate globally relevant features. The usefulness of the approach is demonstrated empirically through a large scale experiment involving 13 classifiers and 24 datasets. Results demonstrate the ability of the method in generating highly informative features and a strong positive effect on the accuracy of the classifiers.
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Létourneau, S., Matwin, S., Famili, A.F. (2008). Generation of Globally Relevant Continuous Features for Classification. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2008. Lecture Notes in Computer Science(), vol 5012. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68125-0_19
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DOI: https://doi.org/10.1007/978-3-540-68125-0_19
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