Boosting the Area under the ROC Curve

Phil Long; Rocco Servedio

2007 NIPS NeurIPS 2007

Boosting the Area under the ROC Curve

Abstract

We show that any weak ranker that can achieve an area under the ROC curve slightly better than 1/2 (which can be achieved by random guessing) can be efﬁ- ciently boosted to achieve an area under the ROC curve arbitrarily close to 1. We further show that this boosting can be performed even in the presence of indepen- dent misclassiﬁcation noise, given access to a noise-tolerant weak ranker.

📈 Trend Setter - Weakly Supervised Learning

🧭 Keyword Pioneer - area under roc

🐝 Cross-Pollinator - Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio

🐣 Hot Topic Early Bird - binary classification

Authors

Phil Long , Rocco Servedio

Topics

Machine Learning > Core Methods > Classification Machine Learning > Learning Types > Weakly Supervised Learning Machine Learning > Optimization & Theory > Learning Theory Machine Learning > Optimization & Theory > Optimization Machine Learning > Learning Types > Ensemble Learning

Keywords

binary classification ensemble learning supervised learning boosting roc curve area under roc weak ranker area under curve weak learner area under the roc curve

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