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Feature selection is among the keys in many applications, especially in mining high-dimensional data. With lack of labeled instances, the learning accuracy may deteriorate using traditional methods. In this paper, we introduce a ldquowrapperrdquo type semi-supervised feature selection approach based on RSC model. It extends the class label from labeled training set to unlabeled data. Additionally,...
Credit risk is the primary source of risk to financial institutions. Support vector machine (SVM) is a good classifier to solve binary classification problem and the learning results possess stronger robustness. The attribute reduction of rough set has been applied as preprocessor, then resolving the problem of the application of SVM in housing loan credit evaluation, such as the choice of kernel...
Since hyper-sphere SVM treat all samples equally, its performance is lower when distribution of the training examples is uneven. How to eliminate the influence of the uneven class sizes is important for the resulting classifier. To solve this problem, we present a new weighted hyper-sphere SVM based on the analysis of performance influence caused by the class size. Experimental results show that our...
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