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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/40168

Title: Genetic-evolved Bayesian networks in a biomedical application
Authors: Chih-Chiang Wei
Contributors: 國立臺灣海洋大學:海洋環境資訊學系
Keywords: Classification
Bayesian network
Genetic algorithm
Date: 2013-01
Issue Date: 2017-01-16T02:46:04Z
Publisher: Advances in Intelligent Systems and Applications
Abstract: Abstract: This study presents genetic algorithm (GA) for discovering Bayesian network structure. The algorithm is applied to a medical datasets for vertebral column. Data set containing values for six biomechanical features is used to classify patients into three categories: disk hernia (DH), spondylolisthesis (SL), and normal (NO) or two categories: abnormal (AB), and NO. On ten-fold crossvalidation run, the average AUC (the area under the ROC curve) measures of 0.874 and 0.923 for two and three categories are obtained, respectively. Results indicate that GA is relatively effective algorithm. Consequently, the GAevolved BN is powerful tool for knowledge representation and inference because the causality relationship can be observed.
Relation: 1
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/40168
Appears in Collections:[海洋環境資訊系] 期刊論文

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