Selective Feature Generation Method for Classification of Low-dimensional Data
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Choi, S. -I. | - |
dc.contributor.author | Choi, S. T. | - |
dc.contributor.author | Yoo, H. | - |
dc.date.available | 2019-01-22T14:13:21Z | - |
dc.date.issued | 2018-02 | - |
dc.identifier.issn | 1841-9836 | - |
dc.identifier.issn | 1841-9844 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/1260 | - |
dc.description.abstract | We propose a method that generates input features to effectively classify low-dimensional data. To do this, we first generate high-order terms for the input features of the original low-dimensional data to form a candidate set of new input features. Then, the discrimination power of the candidate input features is quantitatively evaluated by calculating the 'discrimination distance' for each candidate feature. As a result, only candidates with a large amount of discriminative information are selected to create a new input feature vector, and the discriminant features that are to be used as input to the classifier are extracted from the new input feature vectors by using a subspace discriminant analysis. Experiments on low-dimensional data sets in the UCI machine learning repository and several kinds of low-resolution facial image data show that the proposed method improves the classification performance of low-dimensional data by generating features. | - |
dc.format.extent | 15 | - |
dc.publisher | CCC PUBL-AGORA UNIV | - |
dc.title | Selective Feature Generation Method for Classification of Low-dimensional Data | - |
dc.type | Article | - |
dc.identifier.doi | 10.15837/ijccc.2018.1.2931 | - |
dc.identifier.bibliographicCitation | INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, v.13, no.1, pp 24 - 38 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000425895400002 | - |
dc.identifier.scopusid | 2-s2.0-85042738934 | - |
dc.citation.endPage | 38 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 24 | - |
dc.citation.title | INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL | - |
dc.citation.volume | 13 | - |
dc.type.docType | Article | - |
dc.publisher.location | 루마니아 | - |
dc.subject.keywordAuthor | feature generation | - |
dc.subject.keywordAuthor | input feature selection | - |
dc.subject.keywordAuthor | feature extraction | - |
dc.subject.keywordAuthor | discriminant distance | - |
dc.subject.keywordAuthor | low-dimensional data | - |
dc.subject.keywordAuthor | data classification | - |
dc.subject.keywordPlus | FACE-RECOGNITION | - |
dc.subject.keywordPlus | FEATURE-EXTRACTION | - |
dc.subject.keywordPlus | DISCRIMINANT-ANALYSIS | - |
dc.subject.keywordPlus | PATTERN-RECOGNITION | - |
dc.subject.keywordPlus | ILLUMINATION | - |
dc.subject.keywordPlus | EIGENFACES | - |
dc.subject.keywordPlus | POSE | - |
dc.relation.journalResearchArea | Automation & Control Systems | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Automation & Control Systems | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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