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 performance under decision-tree paradigms by presenting two new models. The intuition behind our work is that probability-based ranking is a relative metric among samples, therefore, distinct probabilities are crucial for accurate ranking. The first model, Lazy Distance-based Tree (LDTree), uses a lazy learner at each leaf to explicitly distinguish the different contributions of leaf samples when estimating the probabilities for an unlabeled sample. The second model, Eager Distance-based Tree (EDTree), improves LDTree by changing it into an eager algorithm. In both models, each unlabeled sample is assigned a set of unique probabilities of class membership instead of a set of uniformed ones, which gives finer resolution to differentiate samples and leads to the improvement of ranking. On 34 UCI sample sets, experi...

• Type: Conference
• Year: 2006
• Where: ICTAI
• Authors: Han Liang, Yuhong Yan
• Keywords: Artificial Intelligence, Distance-based Tree, ICTAI 2006, Probability-based Ranking, Unlabeled Sample