Selecting salient features for classification based on neural network committees
Pattern Recognition Letters
Predictor output sensitivity and feature similarity-based feature selection
Fuzzy Sets and Systems
Increasing classification efficiency with multiple mirror classifiers
Expert Systems with Applications: An International Journal
Evolving Committees of Support Vector Machines
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Multi-class support vector machine for classification of the ultrasonic images of supraspinatus
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
VISUAL'07 Proceedings of the 9th international conference on Advances in visual information systems
The mass appraisal of the real estate by computational intelligence
Applied Soft Computing
Orthogonal relief algorithm for feature selection
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Feature subset selection using improved binary gravitational search algorithm
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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Sequential forward selection (SFS) and sequential backward elimination (SBE) are two commonly used search methods in feature subset selection. In the present study, we derive an orthogonal forward selection (OFS) and an orthogonal backward elimination (OBE) algorithms for feature subset selection by incorporating Gram-Schmidt and Givens orthogonal transforms into forward selection and backward elimination procedures, respectively. The basic idea of the orthogonal feature subset selection algorithms is to find an orthogonal space in which to express features and to perform feature subset selection. After selection, the physically meaningless features in the orthogonal space are linked back to the same number of input variables in the original measurement space. The strength of employing orthogonal transforms is that features are decorrelated in the orthogonal space, hence individual features can be evaluated and selected independently. The effectiveness of our algorithms to deal with real world problems is finally demonstrated.