1. Designing a supervised feature selection technique for mixed attribute data analysis
- Author
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Dong Hyun Jeong, Bong Keun Jeong, Nandi Leslie, Charles Kamhoua, and Soo-Yeon Ji
- Subjects
Mixed-attribute data analysis ,Supervised feature selection ,Machine learning ,Visual analysis ,Cybernetics ,Q300-390 ,Electronic computers. Computer science ,QA75.5-76.95 - 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.
- Published
- 2022
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