Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Floating search methods in feature selection
Pattern Recognition Letters
The nature of statistical learning theory
The nature of statistical learning theory
Divergence Based Feature Selection for Multimodal Class Densities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Input Feature Selection by Mutual Information Based on Parzen Window
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Subset Selection Using a Genetic Algorithm
IEEE Intelligent Systems
Classifier-Independent Feature Selection For Two-Stage Feature Selection
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
An introduction to variable and feature selection
The Journal of Machine Learning Research
Dimensionality reduction via sparse support vector machines
The Journal of Machine Learning Research
Variable selection using svm based criteria
The Journal of Machine Learning Research
Use of the zero norm with linear models and kernel methods
The Journal of Machine Learning Research
Fast Branch & Bound Algorithms for Optimal Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning with many irrelevant features
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
Optimization of k nearest neighbor density estimates
IEEE Transactions on Information Theory
The estimation of the gradient of a density function, with applications in pattern recognition
IEEE Transactions on Information Theory
Expert Systems with Applications: An International Journal
Feature and Classifier Selection in Class Decision Trees
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Expert Systems with Applications: An International Journal
Location determination of mobile devices for an indoor WLAN application using a neural network
Knowledge and Information Systems
Knowledge and Information Systems
Ensemble gene selection for cancer classification
Pattern Recognition
Enhancing the classification accuracy by scatter-search-based ensemble approach
Applied Soft Computing
Designing simulated annealing and subtractive clustering based fuzzy classifier
Applied Soft Computing
Expert Systems with Applications: An International Journal
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Feature selection is used for finding a feature subset that has the most discriminative information from the original feature set. In practice, since we do not know the classifier to be used after feature selection, it is desirable to find a feature subset that is universally effective for any classifier. Such a trial is called classifier-independent feature selection. In this study, we propose a novel classifier-independent feature selection method on the basis of the estimation of Bayes discrimination boundary. The experimental results on 12 real-world datasets showed the fundamental effectiveness of the proposed method.