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• W2084162392 abstract "Feature selection is an essential process in data mining applications since it reduces a model’s complexity. However, feature selection with various types of costs is still a new research topic. In this paper, we study the cost-sensitive feature selection problem of numeric data with measurement errors. The major contributions of this paper are fourfold. First, a new data model is built to address test costs and misclassification costs as well as error boundaries. It is distinguished from the existing models mainly on the error boundaries. Second, a covering-based rough set model with normal distribution measurement errors is constructed. With this model, coverings are constructed from data rather than assigned by users. Third, a new cost-sensitive feature selection problem is defined on this model. It is more realistic than the existing feature selection problems. Fourth, both backtracking and heuristic algorithms are proposed to deal with the new problem. Experimental results show the efficiency of the pruning techniques for the backtracking algorithm and the effectiveness of the heuristic algorithm. This study is a step toward realistic applications of the cost-sensitive learning." @default.
• W2084162392 created "2016-06-24" @default.
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• W2084162392 creator A5058078141 @default.
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• W2084162392 date "2013-01-01" @default.
• W2084162392 modified "2023-10-14" @default.
• W2084162392 title "Cost-Sensitive Feature Selection of Numeric Data with Measurement Errors" @default.
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• W2084162392 doi "https://doi.org/10.1155/2013/754698" @default.
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