Designing a supervised feature selection technique for mixed attribute data analysis
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 criterio...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-12-01
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| Series: | Machine Learning with Applications |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666827022001062 |
| _version_ | 1811199161929826304 |
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| author | Dong Hyun Jeong Bong Keun Jeong Nandi Leslie Charles Kamhoua Soo-Yeon Ji |
| author_facet | Dong Hyun Jeong Bong Keun Jeong Nandi Leslie Charles Kamhoua Soo-Yeon Ji |
| author_sort | Dong Hyun Jeong |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T01:43:21Z |
| format | Article |
| id | doaj.art-e0e0fbdc96384a0088a223dd50338957 |
| institution | Directory Open Access Journal |
| issn | 2666-8270 |
| language | English |
| last_indexed | 2024-04-12T01:43:21Z |
| publishDate | 2022-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Machine Learning with Applications |
| spelling | doaj.art-e0e0fbdc96384a0088a223dd503389572022-12-22T03:53:08ZengElsevierMachine Learning with Applications2666-82702022-12-0110100431Designing a supervised feature selection technique for mixed attribute data analysisDong Hyun Jeong0Bong Keun Jeong1Nandi Leslie2Charles Kamhoua3Soo-Yeon Ji4Department of Computer Science and Information Technology, University of the District of Columbia, DC, USA; Corresponding authors.Department of Management and Decision Sciences, Coastal Carolina University, SC, USARaytheon Technologies, MD, USAU.S. Army Research Laboratory (ARL), Adelphi, MD, USADepartment of Computer Science, Bowie State University, MD, USA; Corresponding authors.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.http://www.sciencedirect.com/science/article/pii/S2666827022001062Mixed-attribute data analysisSupervised feature selectionMachine learningVisual analysis |
| spellingShingle | Dong Hyun Jeong Bong Keun Jeong Nandi Leslie Charles Kamhoua Soo-Yeon Ji Designing a supervised feature selection technique for mixed attribute data analysis Machine Learning with Applications Mixed-attribute data analysis Supervised feature selection Machine learning Visual analysis |
| title | Designing a supervised feature selection technique for mixed attribute data analysis |
| title_full | Designing a supervised feature selection technique for mixed attribute data analysis |
| title_fullStr | Designing a supervised feature selection technique for mixed attribute data analysis |
| title_full_unstemmed | Designing a supervised feature selection technique for mixed attribute data analysis |
| title_short | Designing a supervised feature selection technique for mixed attribute data analysis |
| title_sort | designing a supervised feature selection technique for mixed attribute data analysis |
| topic | Mixed-attribute data analysis Supervised feature selection Machine learning Visual analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2666827022001062 |
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