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» Lazy Learning for Improving Ranking of Decision Trees
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AUSAI
2006
Springer
14 years 1 months ago
Lazy Learning for Improving Ranking of Decision Trees
Decision tree-based probability estimation has received great attention because accurate probability estimation can possibly improve classification accuracy and probability-based r...
Han Liang, Yuhong Yan
ICML
2003
IEEE
14 years 10 months ago
Boosting Lazy Decision Trees
This paper explores the problem of how to construct lazy decision tree ensembles. We present and empirically evaluate a relevancebased boosting-style algorithm that builds a lazy ...
Xiaoli Zhang Fern, Carla E. Brodley
ICTAI
2006
IEEE
14 years 3 months ago
Improve Decision Trees for Probability-Based Ranking by Lazy Learners
Existing work shows that classic decision trees have inherent deficiencies in obtaining a good probability-based ranking (e.g. AUC). This paper aims to improve the ranking perfor...
Han Liang, Yuhong Yan
ICML
1999
IEEE
14 years 10 months ago
Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees
Lbr is a lazy semi-naive Bayesian classi er learning technique, designed to alleviate the attribute interdependence problem of naive Bayesian classi cation. To classify a test exa...
Zijian Zheng, Geoffrey I. Webb, Kai Ming Ting
ICML
2009
IEEE
14 years 4 months ago
Decision tree and instance-based learning for label ranking
The label ranking problem consists of learning a model that maps instances to total orders over a finite set of predefined labels. This paper introduces new methods for label ra...
Weiwei Cheng, Jens C. Huhn, Eyke Hüllermeier