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2004
ACM

A DEA approach for model combination

10 years 6 months ago
A DEA approach for model combination
This paper proposes a novel Data Envelopment Analysis (DEA) based approach for model combination. We first prove that for the 2-class classification problems DEA models identify the same convex hull as the popular ROC analysis used for model combination. For general k-class classifiers, we then develop a DEA-based method to combine multiple classifiers. Experiments show that the method outperforms other benchmark methods and suggest that DEA can be a promising tool for model combination. Categories and Subject Descriptors H.2.8 [Database Management]: Applications ? Data Mining; I.2.6 [Artificial Intelligence]: Learning General Terms Algorithms, Experimentation Keywords Model Combination, Data Envelopment Analysis, ROC
Zhiqiang Zheng, Balaji Padmanabhan, Haoqiang Zheng
Added 30 Nov 2009
Updated 30 Nov 2009
Type Conference
Year 2004
Where KDD
Authors Zhiqiang Zheng, Balaji Padmanabhan, Haoqiang Zheng
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