Transformation of attribute space by function decomposition
Function decomposition is a promising mechanism for machine learning. This paper investigates its use as a redundancy removal and feature construction preprocessor. Experiments show that its combination with naive Bayesian classifier and decision trees is especially successful on artificial domains while results on real-world data are less encouraging.
KeywordsDecision Tree Classification Accuracy Attribute Space Information Gain Bayesian Classifier
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