Document Type
Article
Publication Date
12-15-2022
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.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Dong Hyun Jeong, Bong Keun Jeong, Nandi Leslie, Charles Kamhoua, and Soo-Yeon Ji, Designing a supervised feature selection technique for mixed attribute data analysis, Machine Learning with Applications, Volume 10, 2022, 100431, https://doi.org/10.1016/j.mlwa.2022.100431. Available at https://digitalcommons.coastal.edu/management/
Comments
Elsevier originally published this article.