Fast orthogonal forward selection algorithm for feature subset selection

Authors:
K. Z. Mao
Affiliations:
Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ.
Venue:
IEEE Transactions on Neural Networks
Year:
2002

Citing 0
Cited 7

FS_SFS: A novel feature selection method for support vector machines

Pattern Recognition
Increasing classification efficiency with multiple mirror classifiers

Expert Systems with Applications: An International Journal
A Fast Fuzzy Neural Modelling Method for Nonlinear Dynamic Systems

ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Generalized discriminant analysis: a matrix exponential approach

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A new hybrid ant colony optimization algorithm for feature selection

Expert Systems with Applications: An International Journal
The automatic model selection and variable kernel width for RBF neural networks

Neurocomputing
Efficient feature size reduction via predictive forward selection

Pattern Recognition

Quantified Score

Hi-index	0.01

Visualization

Abstract

Feature selection is an important issue in pattern classification. In the presented study, we develop a fast orthogonal forward selection (FOFS) algorithm for feature subset selection. The FOFS algorithm employs an orthogonal transform to decompose correlations among candidate features, but it performs the orthogonal decomposition in an implicit way. Consequently, the fast algorithm demands less computational effort as compared with conventional orthogonal forward selection (OFS).