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The reduction of attributes is a critical problem in the rough set theory. Finding the minimal reduct is turned out to be a NP-hard problem. Many heuristic algorithms, which use the significance of the condition attribute with reference to the decision attributes as the indication for attribute selection, have been proposed in this area. In this paper the pair-wise complementarity of condition attributes...
In this paper, a novel pattern classification approach is proposed called shortest feature line segment (SFLS). It retains the ideas and advantages of nearest feature line (NFL) and it can suppress the drawbacks of NFL, i.e., the extrapolation inaccuracy, interpolation inaccuracy and high computational complexity. SFLS uses length of the feature line segment satisfying given geometric relation constraints,...
With the increase of the training set??s size, the efficiency of support vector machine (SVM) classifier will be confined. To solve such a problem, a novel pre-extracting method for SVM classification is proposed in this paper. In SVM classification, only support vectors (SVs) have significant influence on the optimization result. We adopt a non-parametric k-NN rule called relative neighborhood graph...
Multiple classifiers fusion is a powerful solution to the difficult and complex classification problems, which can improve performance and generalization capability. This paper presents a multiple k-nearest neighbor classifiers fusion approach based on evidence theory. Independent k-NN classifiers are established based on heterogeneous features. The novel approach to generating mass functions of a...
Multiple Classifiers Fusion is to utilize distinguished classifiers to resolve the same classification problem as a single classifier does, which can improve performance and generalization capability. In this paper, a new method of multiple classifiers fusion based on weighted evidence combination is proposed. Independent member classifiers are designed based on heterogeneous features by utilizing...
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