Abstract
AbstractSuccess of many learning schemes is based on selection of a small set of highly predictive attributes. The inclusion of irrelevant, redundant and noisy attributes in the process model can result in poor predictive accuracy and increased computation. This paper shows that the accuracy of classification can be improved by selecting subsets of strong attributes. Attribute selection is performed by using the Wrapper method with several classification learners. The processed data are classified by diverse learning schemes and generated “if-then” rules are supervised by domain experts.KeywordsDomain ExpertDecision TableAttribute SelectionSick Sinus SyndromeBinary AttributeThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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