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- W2294322969 abstract "AbstractEvaluation functions, used to measure the quality of features, have great influence on the feature selection algorithms in areas of data mining and knowledge discovery. However, the existing evaluation functions are often inadequately measured candidate features on cost-sensitive heterogeneous data. To address this problem, an entropy-based evaluation function is firstly proposed for measuring the uncertainty for heterogeneous data. To further evaluate the quality of candidate features, we propose a multi-criteria based evaluation function, which attempts to find candidate features with the minimal total costs and the same information as the whole feature set. On this basis, a cost-sensitive feature selection algorithm on heterogeneous data is developed. Compared with the existing feature selection algorithms, the experimental results show that the proposed algorithm is more efficient to find a subset of features without losing the classification performance.KeywordsFeature selectionCost-sensitiveEntropyHeterogeneous dataRough sets" @default.
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- W2294322969 date "2015-01-01" @default.
- W2294322969 modified "2023-09-25" @default.
- W2294322969 title "Cost-Sensitive Feature Selection on Heterogeneous Data" @default.
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- W2294322969 doi "https://doi.org/10.1007/978-3-319-18032-8_31" @default.
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