Text Categorization Based on Regularized Linear Classification Methods
Information Retrieval
Robust Object Detection via Soft Cascade
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
An Expected Utility Approach to Active Feature-Value Acquisition
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Cost-sensitive feature acquisition and classification
Pattern Recognition
Partial example acquisition in cost-sensitive learning
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
LungCAD: a clinically approved, machine learning system for lung cancer detection
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Using classifier cascades for scalable e-mail classification
Proceedings of the 8th Annual Collaboration, Electronic messaging, Anti-Abuse and Spam Conference
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We propose a method to train a cascade of classifiers by simultaneously optimizing all its stages. The approach relies on the idea of optimizing soft cascades. In particular, instead of optimizing a deterministic hard cascade, we optimize a stochastic soft cascade where each stage accepts or rejects samples according to a probability distribution induced by the previous stage-specific classifier. The overall system accuracy is maximized while explicitly controlling the expected cost for feature acquisition. Experimental results on three clinically relevant problems show the effectiveness of our proposed approach in achieving the desired tradeoff between accuracy and feature acquisition cost.