Cited time in webofscience Cited time in scopus

Full metadata record

DC Field Value Language 손창식 - 최락현 - 강원석 - 이종하 - 2018-01-24T02:01:01Z - 2018-01-24T02:01:01Z - 2018-01-17 - 2016-12 -
dc.identifier.citation 한국산업정보학회논문지, v.21, no.6, pp.13 - 21 -
dc.identifier.issn 1229-3741 -
dc.identifier.uri -
dc.description.abstract 특징선택은 데이터 마이닝, 기계학습 분야에서 가장 중요한 이슈 중 하나로, 원본 데이터에서가장 좋은 분류 성능을 보여줄 수 있는 특징들을 찾아내는 방법이다. 본 논문에서는 정보 입자성을 기반으로한 neighborhood 러프집합 모델을 이용한 특징선택 방법을 제안한다. 제안된 방법의 효과성은 5,252명의유방 초음파 영상으로부터 추출된 298가지의 특징들 중에서 유방 종양의 진단과 관련된 유용한 특징들을선택하는 문제에 적용되었다. 실험결과 19가지의 진단적 특징을 찾을 수 있었고, 이때에 평균 분류 정확성은97.6%를 보였다.

Feature selection is the one of important issue in the field of data mining and machinelearning. It is the technique to find a subset of features which provides the best classificationperformance, from the source data. We propose a feature subset selection method using the neighborhoodrough set model based on information granularity. To demonstrate the effectiveness of proposed method,it was applied to select the useful features associated with breast tumor diagnosis of 298 shape featuresextracted from 5,252 breast ultrasound images, which include 2,745 benign and 2,507 malignant cases.
Experimental results showed that 19 diagnostic features were strong predictors of breast cancerdiagnosis and then average classification accuracy was 97.6%.
dc.language Korean -
dc.publisher 한국산업정보학회 -
dc.title Neighborhood 러프집합 모델을 활용한 유방종양의 진단적 특징 선택 -
dc.title.alternative A Diagnostic Feature Subset Selection of Breast Tumor Based on Neighborhood Rough Set Model -
dc.type Article -
dc.identifier.doi 10.9723/jksiis.2016.21.6.013 -
dc.type.local Article(Domestic) -
dc.type.rims ART -
dc.description.journalClass 2 -
dc.citation.publicationname 한국산업정보학회논문지 -
dc.identifier.kciid ART002189635 -
dc.contributor.nonIdAuthor 손창식 -
dc.contributor.nonIdAuthor 최락현 -
dc.contributor.nonIdAuthor 이종하 -
dc.identifier.citationVolume 21 -
dc.identifier.citationNumber 6 -
dc.identifier.citationStartPage 13 -
dc.identifier.citationEndPage 21 -
dc.identifier.citationTitle 한국산업정보학회논문지 -
dc.description.isOpenAccess N -
dc.contributor.affiliatedAuthor 손창식 -
dc.contributor.affiliatedAuthor 최락현 -
dc.contributor.affiliatedAuthor 강원석 -
dc.contributor.affiliatedAuthor 이종하 -
Files in This Item:

There are no files associated with this item.

Appears in Collections:
Division of Intelligent Robotics 1. Journal Articles


  • twitter
  • facebook
  • mendeley

Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.