Lazy Learning for Impoving Ranking of Decision Trees

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ConferenceThe 19th Australian Joint Conference on AI (AJCAI06), December 4-6, 2006., Hobart, Australia
AbstractDecision tree-based probability estimation has received great attention because accurate probability estimation can possibly improve classification accuracy and probability-based ranking. In this paper, we aim to improve probability-based ranking under decision tree paradigms using AUC as the evaluation metric. We deploy a lazy probability estimator at each leaf to avoid uniform probability assignment. More importantly, the lazy probability estimator gives higher weights to the leaf samples closer to an unlabeled sample so that the probability estimation of this unlabeled sample is based on its similarities to those leaf samples. The motivation behind it is that ranking is a relative evaluation measurement among a set of samples, therefore, it is reasonable to yield the probability for an unlabeled sample with reference to its extent of similarities to its neighbors. The proposed new decision tree model, LazyTree, outperforms C4.5, its recent improvement C4.4 and their state-of-the-art variants in AUC on a large suite of benchmark sample sets.
Publication date
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number48785
NPARC number5765152
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Record identifierfc9c8d99-eb9f-4b43-86fc-e152b8b05389
Record created2009-03-29
Record modified2016-05-09
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