A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
Floating search methods in feature selection
Pattern Recognition Letters
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Feature Subset Selection Using a Genetic Algorithm
IEEE Intelligent Systems
Phoneme recognition using wavelet based features
Information Sciences—Informatics and Computer Science: An International Journal - Special issue on recent advances in soft computing
An introduction to variable and feature selection
The Journal of Machine Learning Research
Experiments in speech recognition using a modular MLP architecture for acoustic modelling
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Spoken language analysis, modeling and recognition-statistical and adaptive connectionist approaches
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Random subspace method for multivariate feature selection
Pattern Recognition Letters
The common vector approach and its comparison with other subspace methods in case of sufficient data
Computer Speech and Language
A Branch and Bound Algorithm for Feature Subset Selection
IEEE Transactions on Computers
Genetic algorithm-based feature set partitioning for classification problems
Pattern Recognition
Pattern Recognition, Fourth Edition
Pattern Recognition, Fourth Edition
On feature extraction for spam e-mail detection
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
The search for optimal feature set in power quality event classification
Expert Systems with Applications: An International Journal
Feature selection for multi-label naive Bayes classification
Information Sciences: an International Journal
Multivariable stream data classification using motifs and their temporal relations
Information Sciences: an International Journal
A unified methodology for the efficient computation of discrete orthogonal image moments
Information Sciences: an International Journal
Tongue shape classification by geometric features
Information Sciences: an International Journal
Information Sciences: an International Journal
Adaptive nonlinear manifolds and their applications to pattern recognition
Information Sciences: an International Journal
A fast method for the implementation of common vector approach
Information Sciences: an International Journal
Improved binary particle swarm optimization using catfish effect for feature selection
Expert Systems with Applications: An International Journal
Smart pulse wave detection system using intelligence
ICHIT'11 Proceedings of the 5th international conference on Convergence and hybrid information technology
Pixel selection based on discriminant features with application to face recognition
Pattern Recognition Letters
A novel probabilistic feature selection method for text classification
Knowledge-Based Systems
Gender classification from unaligned facial images using support subspaces
Information Sciences: an International Journal
Genetic algorithms in feature and instance selection
Knowledge-Based Systems
The impact of preprocessing on text classification
Information Processing and Management: an International Journal
Double linear regressions for single labeled image per person face recognition
Pattern Recognition
Traffic sign recognition using group sparse coding
Information Sciences: an International Journal
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Feature selection is an essential topic in the field of pattern recognition. The feature selection strategy has a direct influence on the accuracy and processing time of pattern recognition applications. Features can be evaluated with either univariate approaches, which examine features individually, or multivariate approaches, which consider possible feature correlations and examine features as a group. Although univariate approaches do not take the correlation among features into consideration, they can provide the individual discriminatory power of the features, and they are also much faster than multivariate approaches. Since it is crucial to know which features are more or less informative in certain pattern recognition applications, univariate approaches are more useful in these cases. This paper therefore proposes subspace based separability measures to determine the individual discriminatory power of the features. These measures are then employed to sort and select features in a multi-class manner. The feature selection performances of the proposed measures are evaluated and compared with the univariate forms of classic separability measures (Divergence, Bhattacharyya, Transformed Divergence, and Jeffries-Matusita) on several datasets. The experimental results clearly indicate that the new measures yield comparable or even better performance than the classic ones in terms of classification accuracy and dimension reduction rate.