Spherical Classification of Data, a New Rule-Based Learning Method
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ma, Zhengyu | - |
dc.contributor.author | Ryoo, Hong Seo | - |
dc.date.accessioned | 2021-12-08T08:41:51Z | - |
dc.date.available | 2021-12-08T08:41:51Z | - |
dc.date.created | 2021-08-30 | - |
dc.date.issued | 2021 | - |
dc.identifier.issn | 0176-4268 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/130289 | - |
dc.description.abstract | This paper presents a new rule-based classification method that partitions data under analysis into spherical patterns. The forte of the method is twofold. One, it exploits the efficiency of distance metric-based clustering to fast collect similar data into spherical patterns. The other, spherical patterns are each a trait shared among one type of data only, hence are built for classification of new data. Numerical studies with public machine learning datasets from Lichman (2013), in comparison with well-established classification methods from Boros et al. (IEEE Transactions on Knowledge and Data Engineering, 12, 292-306, 2000) and Waikato Environment for Knowledge Analysis (), demonstrate the aforementioned utilities of the new method well. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER | - |
dc.subject | LOGICAL ANALYSIS | - |
dc.subject | NONLINEAR SEPARATION | - |
dc.subject | ALGORITHM | - |
dc.subject | PATTERNS | - |
dc.title | Spherical Classification of Data, a New Rule-Based Learning Method | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Ryoo, Hong Seo | - |
dc.identifier.doi | 10.1007/s00357-019-09355-z | - |
dc.identifier.scopusid | 2-s2.0-85079821274 | - |
dc.identifier.wosid | 000520045300001 | - |
dc.identifier.bibliographicCitation | JOURNAL OF CLASSIFICATION, v.38, no.1, pp.44 - 71 | - |
dc.relation.isPartOf | JOURNAL OF CLASSIFICATION | - |
dc.citation.title | JOURNAL OF CLASSIFICATION | - |
dc.citation.volume | 38 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 44 | - |
dc.citation.endPage | 71 | - |
dc.type.rims | ART | - |
dc.type.docType | Article; Early Access | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | ssci | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalResearchArea | Psychology | - |
dc.relation.journalWebOfScienceCategory | Mathematics, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Psychology, Mathematical | - |
dc.subject.keywordPlus | LOGICAL ANALYSIS | - |
dc.subject.keywordPlus | NONLINEAR SEPARATION | - |
dc.subject.keywordPlus | ALGORITHM | - |
dc.subject.keywordPlus | PATTERNS | - |
dc.subject.keywordAuthor | Supervised learning | - |
dc.subject.keywordAuthor | Classification | - |
dc.subject.keywordAuthor | Spherical pattern | - |
dc.subject.keywordAuthor | Rule induction | - |
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