N-ary decomposition for multi-class classification
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Zhou, Joey Tianyi | - |
dc.contributor.author | Tsang, Ivor W. | - |
dc.contributor.author | Ho, Shen-Shyang | - |
dc.contributor.author | Mueller, Klaus-Robert | - |
dc.date.accessioned | 2021-09-01T15:46:07Z | - |
dc.date.available | 2021-09-01T15:46:07Z | - |
dc.date.created | 2021-06-19 | - |
dc.date.issued | 2019-05 | - |
dc.identifier.issn | 0885-6125 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/65874 | - |
dc.description.abstract | A common way of solving a multi-class classification problem is to decompose it into a collection of simpler two-class problems. One major disadvantage is that with such a binary decomposition scheme it may be difficult to represent subtle between-class differences in many-class classification problems due to limited choices of binary-value partitions. To overcome this challenge, we propose a new decomposition method called N-ary decomposition that decomposes the original multi-class problem into a set of simpler multi-class subproblems. We theoretically show that the proposed N-ary decomposition could be unified into the framework of error correcting output codes and give the generalization error bound of an N-ary decomposition for multi-class classification. Extensive experimental results demonstrate the state-of-the-art performance of our approach. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER | - |
dc.subject | VECTOR MACHINES | - |
dc.subject | BINARY | - |
dc.subject | SVM | - |
dc.title | N-ary decomposition for multi-class classification | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Mueller, Klaus-Robert | - |
dc.identifier.doi | 10.1007/s10994-019-05786-2 | - |
dc.identifier.scopusid | 2-s2.0-85066631663 | - |
dc.identifier.wosid | 000470185100006 | - |
dc.identifier.bibliographicCitation | MACHINE LEARNING, v.108, no.5, pp.809 - 830 | - |
dc.relation.isPartOf | MACHINE LEARNING | - |
dc.citation.title | MACHINE LEARNING | - |
dc.citation.volume | 108 | - |
dc.citation.number | 5 | - |
dc.citation.startPage | 809 | - |
dc.citation.endPage | 830 | - |
dc.type.rims | ART | - |
dc.type.docType | Article; Proceedings Paper | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.subject.keywordPlus | VECTOR MACHINES | - |
dc.subject.keywordPlus | BINARY | - |
dc.subject.keywordPlus | SVM | - |
dc.subject.keywordAuthor | Ensemble learning | - |
dc.subject.keywordAuthor | Multi-class classification | - |
dc.subject.keywordAuthor | N-ary ECOC | - |
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