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Active Learning for Cost-Sensitive Classification
Akshay Krishnamurthy · Alekh Agarwal · Tzu-Kuo Huang · Hal Daumé III · John Langford

Mon Aug 07 01:30 AM -- 05:00 AM (PDT) @ Gallery #142

We design an active learning algorithm for cost-sensitive multiclass classification: problems where different errors have different costs. Our algorithm, COAL, makes predictions by regressing to each label’s cost and predicting the smallest. On a new example, it uses a set of regressors that perform well on past data to estimate possible costs for each label. It queries only the labels that could be the best, ignoring the sure losers. We prove COAL can be efficiently implemented for any regression family that admits squared loss optimization; it also enjoys strong guarantees with respect to predictive performance and labeling effort. Our experiment with COAL show significant improvements in labeling effort and test cost over passive and active baselines.

Author Information

Akshay Krishnamurthy (UMass)
Alekh Agarwal (Microsoft Research)
Tzu-Kuo Huang (Uber)
Hal Daumé III (University of Maryland)
John Langford (Microsoft Research)

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