Scalable Semi-Supervised Aggregation of Classifiers

2015/06/18 by Akshay Balsubramani, Yoav Freund, Balsubramani, Akshay +1 · 2 citations
Computer Science · #Data Stream Mining Techniques #FOS: Computer and information sciences #Face and Expression Recognition #Machine Learning (cs.LG) #Machine Learning and Data Classification

paper · pdf · doi:10.48550/arxiv.1506.05790

openalex publication_date 2015/06/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We present and empirically evaluate an efficient algorithm that learns to aggregate the predictions of an ensemble of binary classifiers. The algorithm uses the structure of the ensemble predictions on unlabeled data to yield significant performance improvements. It does this without making assumptions on the structure or origin of the ensemble, without parameters, and as scalably as linear learning. We empirically demonstrate these performance gains with random forests.

Citations

• A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting
• ImageNet Large Scale Visual Recognition Challenge

Cited by

• Learning from Multiple Noisy Partial Labelers
• Inferring Generative Model Structure with Static Analysis

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