Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Floating search methods in feature selection
Pattern Recognition Letters
Subspace classifier in the Hilbert space
Pattern Recognition Letters
Matrix analysis and applied linear algebra
Matrix analysis and applied linear algebra
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
LS Bound based gene selection for DNA microarray data
Bioinformatics
Filter versus wrapper gene selection approaches in DNA microarray domains
Artificial Intelligence in Medicine
Nonlinear kernel-based statistical pattern analysis
IEEE Transactions on Neural Networks
Optimizing the kernel in the empirical feature space
IEEE Transactions on Neural Networks
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This paper proposes a new filter approach to gene subset selection for kernel-based classifiers. We derive kernel forms of several well-known class separability criteria, and gene subset selection based on the kernelized criteria is applied to microarray cancer classification problems. The performance of our proposed strategy is compared in experiments with those of the conventional filter approach as well as gene ranking methods.