Multi-Choice Wavelet Thresholding Based Binary Classification Method
- Title
- Multi-Choice Wavelet Thresholding Based Binary Classification Method
- Author
- 백승현
- Keywords
- data mining; search procedures; optimization; classification analysis; multi-choice wavelet thresholding
- Issue Date
- 2020-06
- Publisher
- PSYCHOPEN
- Citation
- METHODOLOGY-EUROPEAN JOURNAL OF RESEARCH METHODS FOR THE BEHAVIORAL AND SOCIAL SCIENCES, v. 16, No. 2, Page. 127-146
- Abstract
- Data mining is one of the most effective statistical methodologies to investigate a variety of problems in areas including pattern recognition, machine learning, bioinformatics, chemometrics, and statistics. In particular, statistically-sophisticated procedures that emphasize on reliability of results and computational efficiency are required for the analysis of high-dimensional data. Optimization principles can play a significant role in the rationalization and validation of specialized data mining procedures. This paper presents a novel methodology which is Multi-Choice Wavelet Thresholding (MCWT) based three-step methodology consists of three processes: perception (dimension reduction), decision (feature ranking), and cognition (model selection). In these steps three concepts known as wavelet thresholding, support vector machines for classification and information complexity are integrated to evaluate learning models. Three published data sets are used to illustrate the proposed methodology. Additionally, performance comparisons with recent and widely applied methods are shown.
- URI
- https://meth.psychopen.eu/index.php/meth/article/view/2787https://repository.hanyang.ac.kr/handle/20.500.11754/163405
- ISSN
- 1614-1881; 1614-2241
- DOI
- 10.5964/meth.2787
- Appears in Collections:
- COLLEGE OF BUSINESS AND ECONOMICS[E](경상대학) > BUSINESS ADMINISTRATION(경영학부) > Articles
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