Name
Title | ||
|---|---|---|
An adaptive kernel-based weighted extreme learning machine approach for effective detection of Parkinson's disease. |
Abstract | ||
|---|---|---|
•An adaptive kernel-based weighted extreme learning machine approach is proposed for Parkinson’s disease (PD) diagnosis.•Weighted strategy and non-linear mapping of kernel function are used for handling imbalanced data and improving extent of linear separation.•Both binary version and continuous version of an adaptive ABC algorithm are used for performing feature selection and parameters optimization.•The effectiveness of the proposed method has been evaluated on PD data set in accordance with specificity, sensitivity, ACC, G-mean and F-measure.•We have achieved better performance than existing methods in the literature. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.bspc.2017.06.015 | Biomedical Signal Processing and Control |
Keywords | Field | DocType |
Parkinson’s disease,Imbalanced data,Extreme learning machine,Artificial bee colony,Feature selection | Intrusion,Feature selection,Adaptive kernel,Pattern recognition,Extreme learning machine,Effective method,Artificial intelligence,Classifier (linguistics),Mathematics,Machine learning,Kernel (statistics),Binary number | Journal |
Volume | ISSN | Citations |
38 | 1746-8094 | 2 |
PageRank | References | Authors |
0.35 | 24 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yang Wang | 1 | 4 | 2.75 |
| Anna Wang | 2 | 18 | 8.04 |
| Qing Ai | 3 | 8 | 3.85 |
| Haijing Sun | 4 | 4 | 2.41 |