| Autor: |
Dong Hyun Jeong, Bong Keun Jeong, Nandi Leslie, Charles Kamhoua, Soo-Yeon Ji |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Machine Learning with Applications, Vol 10, Iss , Pp 100431- (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2666-8270 |
| DOI: |
10.1016/j.mlwa.2022.100431 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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