Publication: Are we losing accuracy while gaining confidence in induced rules – an assessment of PrIL
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Wallace, W
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Abstract
Probabilistic Inductive Learning (PrIL), is a tree induction algorithm that provides a minimum correct classification level with a specified confidence for each rule in the decision tree, This feature is particularly useful in uncertain environments where decisions are based on the induced rules. This paper provides a concise description of (the extended) PrIL and demonstrates that its performance is as good as best results in the machine learning literature, using datasets from the data repository at UC Irvine.
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AAAI Press
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Machine learning
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KDD 1995 - Proceedings of the 1st International Conference on Knowledge Discovery and Data Mining
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Except where otherwised noted, this item's license is described as Editöryel Kontrolde bakılacak (Bu alan ilgili koleksiyona geçirilirken boşaltılıp öyle atılacak drop-down menü sonrasında ilgili koleksiyonda gelerek doğru alan seçilecek.)