A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
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
Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
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Feature Subset Selection Using a Genetic Algorithm
IEEE Intelligent Systems
An introduction to variable and feature selection
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Linear Dimensionality Reduction via a Heteroscedastic Extension of LDA: The Chernoff Criterion
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Subclass Discriminant Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparison of Seven Techniques for Choosing Subsets of Pattern Recognition Properties
IEEE Transactions on Computers
A Branch and Bound Algorithm for Feature Subset Selection
IEEE Transactions on Computers
Feature analysis and classification of lymph nodes
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part III
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Linear discriminant analysis (LDA) is often used to produce an effective linear feature extractor for classification. However, some approaches of LDA, such as Fisher's linear discriminant, are not robust to outlier classes. In this paper, a novel approach is proposed to robustly produce an effective linear feature extractor by integrating the discriminatory information from the global and pairwise approaches of LDA. The discriminatory information is integrated either by the sequential forward floating selection algorithm with a criterion function based on the Chernoff bound or by ranking the discriminatory information using the kernel QR factorization with column pivoting according to the indication of an applicability index for these two methods. The proposed approach was compared to various methods of LDA. The experimental results have shown the robustness of the proposed approach and proved the feasibility of the proposed approach.