DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yoon, Y | - |
dc.contributor.author | Lee, GG | - |
dc.date.accessioned | 2016-04-01T01:47:08Z | - |
dc.date.available | 2016-04-01T01:47:08Z | - |
dc.date.created | 2009-08-21 | - |
dc.date.issued | 2007-03 | - |
dc.identifier.issn | 0306-4573 | - |
dc.identifier.other | 2006-OAK-0000006410 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/23697 | - |
dc.description.abstract | In practical text classification tasks, the ability to interpret the classification result is as important as the ability to classify exactly. Associative classifiers have many favorable characteristics such as rapid training, good classification accuracy, and excellent interpretation. However, associative classifiers also have some obstacles to overcome when they are applied in the area of text classification. The target text collection generally has a very high dimension, thus the training process might take a very long time. We propose a feature selection based on the mutual information between the word and class variables to reduce the space dimension of the associative classifiers. In addition, the training process of the associative classifier produces a huge amount of classification rules, which makes the prediction with a new document ineffective. We resolve this by introducing a new efficient method for storing and pruning classification rules. This method can also be used when predicting a test document. Experimental results using the 20-newsgroups dataset show many benefits of the associative classification in both training and predicting when applied to a real world problem. (c) 2006 Elsevier Ltd. All rights reserved. | - |
dc.description.statementofresponsibility | X | - |
dc.language | English | - |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | - |
dc.relation.isPartOf | INFORMATION PROCESSING & MANAGEMENT (postech rank 1) | - |
dc.subject | text classification | - |
dc.subject | associative classifier | - |
dc.subject | feature selection | - |
dc.subject | rule pruning | - |
dc.subject | subset expansion | - |
dc.title | Efficient implementation of associative classifiers for document classification | - |
dc.type | Article | - |
dc.contributor.college | 컴퓨터공학과 | - |
dc.identifier.doi | 10.1016/j.ipm.2006.07.012 | - |
dc.author.google | Yoon, Y | - |
dc.author.google | Lee, GG | - |
dc.relation.volume | 43 | - |
dc.relation.issue | 2 | - |
dc.relation.startpage | 393 | - |
dc.relation.lastpage | 405 | - |
dc.contributor.id | 10103841 | - |
dc.relation.journal | INFORMATION PROCESSING & MANAGEMENT (postech rank 1) | - |
dc.relation.index | SCI급, SCOPUS 등재논문 | - |
dc.relation.sci | SCIE | - |
dc.collections.name | Conference Papers | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | INFORMATION PROCESSING & MANAGEMENT (postech rank 1), v.43, no.2, pp.393 - 405 | - |
dc.identifier.wosid | 000242422100009 | - |
dc.date.tcdate | 2019-01-01 | - |
dc.citation.endPage | 405 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 393 | - |
dc.citation.title | INFORMATION PROCESSING & MANAGEMENT (postech rank 1) | - |
dc.citation.volume | 43 | - |
dc.contributor.affiliatedAuthor | Lee, GG | - |
dc.identifier.scopusid | 2-s2.0-33750478328 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.wostc | 8 | - |
dc.type.docType | Article; Proceedings Paper | - |
dc.subject.keywordAuthor | text classification | - |
dc.subject.keywordAuthor | associative classifier | - |
dc.subject.keywordAuthor | feature selection | - |
dc.subject.keywordAuthor | rule pruning | - |
dc.subject.keywordAuthor | subset expansion | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Information Science & Library Science | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Information Science & Library Science | - |
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