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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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