Regression level set estimation via cost-sensitive classification

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Title: Regression level set estimation via cost-sensitive classification
Author: Scott, Clayton D.; Davenport, Mark A.
Type: Journal article
Citation: C. D. Scott and M. A. Davenport, "Regression level set estimation via cost-sensitive classification," IEEE Transactions on Signal Processing, vol. 55, no. 6, pp. 2752-2757, 2007.
Abstract: Regression level set estimation is an important yet understudied learning task. It lies somewhere between regression function estimation and traditional binary classification, and in many cases is a more appropriate setting for questions posed in these more common frameworks. This note explains how estimating the level set of a regression function from training examples can be reduced to cost-sensitive classification. We discuss the theoretical and algorithmic benefits of this learning reduction, demonstrate several desirable properties of the associated risk, and report experimental results for histograms, support vector machines, and nearest neighbor rules on synthetic and real data.
Date Published: 2007-06-01

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  • DSP Publications [508 items]
    Publications by Rice Faculty and graduate students in digital signal processing.