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KMID : 1137820100310010040
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의공학회지
2010 Volume.31 No. 1 p.40 ~ p.49
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Rule Weight-Based Fuzzy Classification Model for Analyzing Admission-Discharge of Dyspnea Patients
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Son Chang-Sik
Shin A-Mi
Lee Young-Dong
Park Hyoung-Seob
Park Hee-Joon
Kim Yoon-Nyun
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Abstract
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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.
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KEYWORD
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Fuzzy Classification Model, Rule Weight, Rule Generation, Dyspnea Patient
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