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摘要:
We have presented an integrated approach based on supervised and unsupervised learning tech- nique to improve the accuracy of six predictive models. They are developed to predict outcome of tuberculosis treatment course and their accuracy needs to be improved as they are not precise as much as necessary. The integrated supervised and unsupervised learning method (ISULM) has been proposed as a new way to improve model accuracy. The dataset of 6450 Iranian TB patients under DOTS therapy was applied to initially select the significant predictors and then develop six predictive models using decision tree, Bayesian network, logistic regression, multilayer perceptron, radial basis function, and support vector machine algorithms. Developed models have integrated with k-mean clustering analysis to calculate more accurate predicted outcome of tuberculosis treatment course. Obtained results, then, have been evaluated to compare prediction accuracy before and after ISULM application. Recall, Precision, F-measure, and ROC area are other criteria used to assess the models validity as well as change percentage to show how different are models before and after ISULM. ISULM led to improve the prediction accuracy for all applied classifiers ranging between 4% and 10%. The most and least improvement for prediction accuracy were shown by logistic regression and support vector machine respectively. Pre-learning by k- mean clustering to relocate the objects and put similar cases in the same group can improve the classification accuracy in the process of integrating supervised and unsupervised learning.
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篇名 Improvement the Accuracy of Six Applied Classification Algorithms through Integrated Supervised and Unsupervised Learning Approach
来源期刊 电脑和通信(英文) 学科 医学
关键词 ISULM INTEGRATION Supervised and UNSUPERVISED Learning Classification ACCURACY TUBERCULOSIS
年,卷(期) 2014,(4) 所属期刊栏目
研究方向 页码范围 201-209
页数 9页 分类号 R73
字数 语种
DOI
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研究主题发展历程
节点文献
ISULM
INTEGRATION
Supervised
and
UNSUPERVISED
Learning
Classification
ACCURACY
TUBERCULOSIS
研究起点
研究来源
研究分支
研究去脉
引文网络交叉学科
相关学者/机构
期刊影响力
电脑和通信(英文)
月刊
2327-5219
武汉市江夏区汤逊湖北路38号光谷总部空间
出版文献量(篇)
783
总下载数(次)
0
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