A partition bagging ensemble learning algorithm for Parkinson's speech data mining

Methods for achieving diagnosis of Parkinson's disease (PD) based on speech data mining have been proven effective in recent years. However, due to factors such as the degree of disease of the data collection subjects and the collection equipment and environment, there are different categories...

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Published in:Sheng wu yi xue gong cheng xue za zhi Vol. 36; no. 4; p. 548
Main Authors: Li, Yongming, Zhang, Cheng, Wang, Pin, Xie, Tingjie, Zeng, Xiaoping, Zhang, Yanling, Cheng, Oumei, Yan, Fang
Format: Journal Article
Language:Chinese
Published: China 25.08.2019
Subjects:
ensemble learning
partition bagging boosting mechanism
classification
Parkinson’s disease
speech data
ISSN:1001-5515
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Abstract Methods for achieving diagnosis of Parkinson's disease (PD) based on speech data mining have been proven effective in recent years. However, due to factors such as the degree of disease of the data collection subjects and the collection equipment and environment, there are different categories of sample aliasing in the sample space of the acquired data set. Samples in the aliased area are difficult to be identified effectively, which seriously affects the classification accuracy of the algorithm. In order to solve this problem, a partition bagging ensemble learning is proposed in this article, which measures the aliasing degree of the sample by designing the the ratio of sample centroid distance metrics and divides the training set into multiple subsets. And then the method of transfer training of misclassified samples is used to adjust the results of subset partitioning. Finally, the optimized weights of each sub-classifier are used to integrate the test results. The experimental results show that the classi
AbstractList Methods for achieving diagnosis of Parkinson's disease (PD) based on speech data mining have been proven effective in recent years. However, due to factors such as the degree of disease of the data collection subjects and the collection equipment and environment, there are different categories of sample aliasing in the sample space of the acquired data set. Samples in the aliased area are difficult to be identified effectively, which seriously affects the classification accuracy of the algorithm. In order to solve this problem, a partition bagging ensemble learning is proposed in this article, which measures the aliasing degree of the sample by designing the the ratio of sample centroid distance metrics and divides the training set into multiple subsets. And then the method of transfer training of misclassified samples is used to adjust the results of subset partitioning. Finally, the optimized weights of each sub-classifier are used to integrate the test results. The experimental results show that the classi
Author Cheng, Oumei
Li, Yongming
Zhang, Yanling
Zeng, Xiaoping
Zhang, Cheng
Xie, Tingjie
Yan, Fang
Wang, Pin
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  organization: School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, P.R.China;Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing 400044, P.R.China.yongmingli@cqu.edu.cn
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  organization: School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, P.R.China
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partition bagging boosting mechanism
classification
Parkinson’s disease
speech data
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Snippet Methods for achieving diagnosis of Parkinson's disease (PD) based on speech data mining have been proven effective in recent years. However, due to factors...
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Title A partition bagging ensemble learning algorithm for Parkinson's speech data mining
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