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.
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