Algorithms for Filtration of Unordered Sets of Regression Rules
作者： Łukasz Wróbel , Marek Sikora , Adam Skowron
DOI: 10.1007/978-3-642-35455-7_26
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摘要: This paper presents six filtration algorithms for the pruning of unordered sets regression rules. Three these aim at elimination rules which cover similar subsets examples, whereas other three ones optimization rule according to prediction accuracy. The effectiveness was empirically tested 5 different learning heuristics on 35 benchmark datasets. results show that, depending algorithm, reduction number fluctuates average between 10% and 50% in most cases it does not cause statistically significant degradation accuracy predictions.
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