Simultaneous feature selection and classification via Minimax Probability Machine

DOI

10.2991/ijcis.2010.3.6.6How to use a DOI?

Keywords

Minimax probability machine, Feature selection, Probability of misclassification, Machine learning.

Abstract

This paper presents a novel method for simultaneous feature selection and classification by incorporating a robust L1-norm into the objective function of Minimax Probability Machine (MPM). A fractional programming framework is derived by using a bound on the misclassification error involving the mean and covariance of the data. Furthermore, the problems are solved by the Quadratic Interpolation method. Experiments show that our methods can select fewer features to improve the generalization compared to MPM, which illustrates the effectiveness of the proposed algorithms.

Copyright

© 2010, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Liming Yang
AU  - Laisheng Wang
AU  - Yuhua Sun
AU  - Ruiyan Zhang
PY  - 2010
DA  - 2010/12/01
TI  - Simultaneous feature selection and classification via Minimax Probability Machine
JO  - International Journal of Computational Intelligence Systems
SP  - 754
EP  - 760
VL  - 3
IS  - 6
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2010.3.6.6
DO  - 10.2991/ijcis.2010.3.6.6
ID  - Yang2010
ER  -