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INFORMS
1998

Feature Selection via Mathematical Programming

13 years 11 months ago
Feature Selection via Mathematical Programming
The problem of discriminating between two nite point sets in n-dimensional feature space by a separating plane that utilizes as few of the features as possible, is formulated as a mathematical program with a parametric objective function and linear constraints. The step function that appears in the objective function can be approximated by a sigmoid or by a concave exponential on the nonnegative real line, or it can be treated exactly by considering the equivalent linear program with equilibrium constraints (LPEC). Computational tests of these three approaches on publicly available real-world databases have been carried out and compared with an adaptation of the optimal brain damage (OBD) method for reducing neural network complexity. One feature selection algorithm via concave minimization (FSV) reduced cross-validation error on a cancer prognosis database by 35.4% while reducing problem features from 32 to 4. Feature selection is an important problem in machine learning 18, 15, 16, ...
Paul S. Bradley, Olvi L. Mangasarian, W. Nick Stre
Added 22 Dec 2010
Updated 22 Dec 2010
Type Journal
Year 1998
Where INFORMS
Authors Paul S. Bradley, Olvi L. Mangasarian, W. Nick Street
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