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- W4307852378 abstract "Identifying optimal features is critical for increasing the overall performance of data classification. This paper introduces a supervised feature selection technique for analyzing mixed attribute data. It measures data classification performances of features with a user-defined performance criterion and determines optimal features to boost the overall data analysis performance. A performance evaluation is managed to highlight the usefulness of the technique with existing feature selection techniques such as analysis of variance test, chi-square test, principal component analysis, and mutual information. Visualization is also utilized to understand the differences in classifying instances with different features. From a comparative performance testing and evaluation, we found 5 ∼ 10% performance improvements with the proposed technique. Overall, evaluation results showed the usefulness of our proposed feature selection technique in mixed attribute data analysis." @default.
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- W4307852378 date "2022-12-01" @default.
- W4307852378 modified "2023-10-16" @default.
- W4307852378 title "Designing a supervised feature selection technique for mixed attribute data analysis" @default.
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- W4307852378 doi "https://doi.org/10.1016/j.mlwa.2022.100431" @default.
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