Preprocessing by cost-sensitive literal reduction algorithm: Reduce (CROSBI ID 23656)
Prilog u knjizi | izvorni znanstveni rad
Podaci o odgovornosti
Lavrač, Nada ; Gamberger, Dragan ; Turney, Peter
engleski
Preprocessing by cost-sensitive literal reduction algorithm: Reduce
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.
machine learning, literals, genetic algorithm
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Podaci o prilogu
179-196-x.
objavljeno
Podaci o knjizi
Learning, Networks and Statistics
Della Riccia, G ; Lenz, H.J ; Kruse, R.
Beč : New York (NY): Springer
1996.