Optimally Combining Classifiers Using Unlabeled Data

Akshay Balsubramani; Yoav Freund

2015 COLT COLT 2015

Optimally Combining Classifiers Using Unlabeled Data

Abstract

We develop a worst-case analysis of aggregation of classifier ensembles for binary classification. The task of predicting to minimize error is formulated as a game played over a given set of unlabeled data (a transductive setting), where prior label information is encoded as constraints on the game. The minimax solution of this game identifies cases where a weighted combination of the classifiers can perform significantly better than any single classifier.

🧭 Keyword Pioneer — weighted combination

🐣 Hot Topic Early Bird — binary classification

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

Authors

Akshay Balsubramani , Yoav Freund

Topics

Machine Learning > Core Methods > Classification Machine Learning > Learning Types > Semi-Supervised Learning Machine Learning > Optimization & Theory > Theory Machine Learning > Core Methods > Ensemble Methods Machine Learning > Learning Types > Classification Machine Learning > Learning Paradigms > Semi-Supervised Learning

Keywords

semi-supervised learning binary classification ensemble learning transductive learning classifier aggregation classifier ensemble weighted combination transductive setting

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