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DC Field Value Language 손창식 - 신아미 - 이영동 - 박형섭 - 박희준 - 김윤년 - 2021-07-20T20:05:02Z - 2021-07-20T20:05:02Z - 2020-11-05 - 2010-02 -
dc.identifier.issn 1229-0807 -
dc.identifier.uri -
dc.description.abstract A rule weight-based fuzzy classification model is proposed to analyze the patterns of admission-discharge of patients as a previous research for differential diagnosis of dyspnea. The proposed model is automatically generated from a labeled data set, supervised learning strategy, using three procedure methodology: i) select fuzzy partition regions from spatial distribution of data; ii) generate fuzzy membership functions from the selected partition regions; and iii) extract a set of candidate rules and resolve a conflict problem among the candidate rules. The effectiveness of the proposed fuzzy classification model was demonstrated by comparing the experimental results for the dyspnea patients' data set with 11 features selected from 55 features by clinicians with those obtained using the conventional classification methods, such as standard fuzzy classifier without rule weights, C4.5, QDA, kNN, and SVMs. -
dc.language Korean -
dc.publisher 대한의용생체공학회 -
dc.title 호흡곤란환자의 입-퇴원 분석을 위한 규칙가중치 기반 퍼지 분류모델 -
dc.title.alternative Rule Weight-Based Fuzzy Classification Model for Analyzing Admission-Discharge of Dyspnea Patients -
dc.type Article -
dc.identifier.bibliographicCitation 의공학회지, v.31, no.1, pp.40 - 49 -
dc.identifier.kciid ART001420612 -
dc.description.isOpenAccess FALSE -
dc.subject.keywordAuthor Fuzzy Classification Model -
dc.subject.keywordAuthor Rule Weight -
dc.subject.keywordAuthor Rule Generation -
dc.subject.keywordAuthor Dyspnea Patient -
dc.citation.endPage 49 -
dc.citation.number 1 -
dc.citation.startPage 40 -
dc.citation.title 의공학회지 -
dc.citation.volume 31 -
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Division of Intelligent Robotics 1. Journal Articles


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