Sciweavers

ICASSP
2011
IEEE

Geometric programming for aggregation of binary classifiers

12 years 7 months ago
Geometric programming for aggregation of binary classifiers
Multiclass classification problems are often decomposed into multiple binary problems that are solved by individual binary classifiers whose results are integrated into a final answer. We present a convex optimization-based method for aggregating results of binary classifiers in an optimal way to estimate class membership probabilities. We model the class membership probability as a softmax function whose input argument is a conic combination of discrepancies induced by individual binary classifiers. With this model, we formulate the 1-regularized maximum likelihood estimation as a convex optimization that is solved by geometric programming. Numerical experiments on several UCI datasets demonstrate the high performance of our method, compared to existing methods.
Sunho Park, Seungjin Choi
Added 21 Aug 2011
Updated 21 Aug 2011
Type Journal
Year 2011
Where ICASSP
Authors Sunho Park, Seungjin Choi
Comments (0)