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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© 1997 Springer-Verlag Wien
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Lavrac, N., Gamberger, D., Turney, P. (1997). Preprocessing by a Cost-Sensitive Literal Reduction Algorithm: Reduce. In: Della Riccia, G., Lenz, HJ., Kruse, R. (eds) Learning, Networks and Statistics. International Centre for Mechanical Sciences, vol 382. Springer, Vienna. https://doi.org/10.1007/978-3-7091-2668-4_11
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DOI: https://doi.org/10.1007/978-3-7091-2668-4_11
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