Active Classification based on Value of Classifier

author: Tianshi Gao, Department of Electrical Engineering, Stanford University
published: Sept. 6, 2012,   recorded: December 2011,   views: 3127
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Description

Modern classification tasks usually involve many class labels and can be informed by a broad range of features. Many of these tasks are tackled by constructing a set of classifiers, which are then applied at test time and then pieced together in a fixed procedure determined in advance or at training time. We present an active classification process at the test time, where each classifier in a large ensemble is viewed as a potential observation that might inform our classification process. Observations are then selected dynamically based on previous observations, using a value-theoretic computation that balances an estimate of the expected classification gain from each observation as well as its computational cost. The expected classification gain is computed using a probabilistic model that uses the outcome from previous observations. This active classification process is applied at test time for each individual test instance, resulting in an efficient instance-specific decision path. We demonstrate the benefit of the active scheme on various real-world datasets, and show that it can achieve comparable or even higher classification accuracy at a fraction of the computational costs of traditional methods.

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Download slides icon Download slides: nips2011_gao_classifier_01.pdf (337.2 KB)

Download article icon Download article: nips2011_0647.pdf (137.2 KB)


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