Abstract
This study is concerned with whether it is possible to detect what information contained in the training data and background knowledge is relevant for solving the learning problem, and whether irrelevant information can be eliminated in preprocessing before starting the learning process. A case study of data preprocessing for a hybrid genetic algorithm shows that the elimination of irrelevant features can substantially improve the efficiency of learning. In addition, cost-sensitive feature elimination can be effective for reducing costs of induced hypotheses.
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© 1996 Springer-Verlag Berlin Heidelberg
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Lavrač, N., Gamberger, D., Turney, P. (1996). Cost-sensitive feature reduction applied to a hybrid genetic algorithm. In: Arikawa, S., Sharma, A.K. (eds) Algorithmic Learning Theory. ALT 1996. Lecture Notes in Computer Science, vol 1160. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61863-5_40
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DOI: https://doi.org/10.1007/3-540-61863-5_40
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