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In this paper we propose a novel support vector machine (SVM) with class-dependent features. According to an importance measure, e.g., the RELIEF weight measure or class separability measure, we rank the features importance for each class against the rest of classes. For each class we select an optimal feature subset using a classifier, e.g., the support vector machine (SVM). For the classification on these class-dependent feature subsets, we propose to construct a novel SVM using "one-against-all" in 2 processes: (1) construct one model for each class by training the classifier with the class's optimal feature subset; (2) during testing, each test pattern is tested on all models and the model with the maximum output decides the class of the test pattern. The method's performance is evaluated on two benchmark datasets. Our results indicate that our novel SVM classifier can effectively realize the classification of class-dependent feature subsets found by our wrapper approach which can remove irrelevant features for each class and at the same time maintain or even improve the classification accuracy in comparison with other feature selection methods.