Floating search methods in feature selection
Pattern Recognition Letters
Analysis of Class Separation and Combination of Class-Dependent Features for Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive floating search methods in feature selection
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
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
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Algorithms for Feature Selection: An Evaluation
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
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
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In pattern recognition, feature selection is an important technique for reducing the measurement cost of features or for improving the performance of classifiers, or both. Removal of features with no discriminative information is effective for improving the precision of estimated parameters of parametric classifiers. Many feature selection algorithms choose a feature subset that is useful for all classes in common. However, the best feature subset for separating one group of classes from another may depend on groups. In this study, we investigate the effectiveness of choosing feature subsets depending on groups of classes (class-dependent features), and propose a classifier system that is built as a decision tree in which nodes have class-dependent feature subsets.