Recognition algorithm of Parkinson's disease based on weighted local discriminant preservation projection embedded ensemble algorithm
Conference: BIBE 2019 - The Third International Conference on Biological Information and Biomedical Engineering
06/20/2019 - 06/22/2019 at Hangzhou, China
Proceedings: BIBE 2019
Pages: 6Language: englishTyp: PDFPersonal VDE Members are entitled to a 10% discount on this title
Liu, Yuchuan; Tan, Xiaoheng; Wang, Pin; Li, Yongming (School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China)
Zhang, Yanling (First Affiliated Hospital of Army Medical University, Chongqing, China)
Machine learning is an important tool for data analysis and mining in Parkinson. The current problems with Parkinson's disease data are high redundancy, high noise, and small sample size. Dimension reduction can effectively solve these problems. However, there are few literatures on dimensionality reduction methods for data analysis and mining of Parkinson's disease, meanwhile the stability and accuracy are not satisfactory. In order to solve this problem, this paper proposes a weighted local discriminant preservation projection embedded ensemble algorithm. Compared with the existing feature selection and feature extraction algorithm, the algorithm proposed in this paper can significantly improve the diagnostic accuracy of Parkinson's disease.