Multi-Choice Wavelet Thresholding Based Binary Classification Method
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Baek, Seung Hyun | - |
dc.contributor.author | Garcia-Diaz, Alberto | - |
dc.contributor.author | Dai, Yuanshun | - |
dc.date.accessioned | 2021-06-22T09:03:58Z | - |
dc.date.available | 2021-06-22T09:03:58Z | - |
dc.date.created | 2021-01-22 | - |
dc.date.issued | 2020-06 | - |
dc.identifier.issn | 1614-1881 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/1062 | - |
dc.description.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. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Hogrefe & Huber Publishers | - |
dc.title | Multi-Choice Wavelet Thresholding Based Binary Classification Method | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Baek, Seung Hyun | - |
dc.identifier.doi | 10.5964/meth.2787 | - |
dc.identifier.scopusid | 2-s2.0-85093503836 | - |
dc.identifier.wosid | 000594093100003 | - |
dc.identifier.bibliographicCitation | Methodology, v.16, no.2, pp.127 - 146 | - |
dc.relation.isPartOf | Methodology | - |
dc.citation.title | Methodology | - |
dc.citation.volume | 16 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 127 | - |
dc.citation.endPage | 146 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | ssci | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Mathematical Methods In Social Sciences | - |
dc.relation.journalResearchArea | Psychology | - |
dc.relation.journalWebOfScienceCategory | Social Sciences, Mathematical Methods | - |
dc.relation.journalWebOfScienceCategory | Psychology, Mathematical | - |
dc.subject.keywordPlus | DISCRIMINANT-ANALYSIS | - |
dc.subject.keywordPlus | OPERATIONS-RESEARCH | - |
dc.subject.keywordPlus | REDUCTION | - |
dc.subject.keywordPlus | GENE | - |
dc.subject.keywordAuthor | data mining | - |
dc.subject.keywordAuthor | search procedures | - |
dc.subject.keywordAuthor | optimization | - |
dc.subject.keywordAuthor | classification analysis | - |
dc.subject.keywordAuthor | multi-choice wavelet thresholding | - |
dc.identifier.url | https://meth.psychopen.eu/index.php/meth/article/view/2787 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
55 Hanyangdeahak-ro, Sangnok-gu, Ansan, Gyeonggi-do, 15588, Korea+82-31-400-4269 sweetbrain@hanyang.ac.kr
COPYRIGHT © 2021 HANYANG UNIVERSITY. ALL RIGHTS RESERVED.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.