Residential College | false |
Status | 已發表Published |
A Hybrid Feature Selection Algorithm Based on a Discrete Artificial Bee Colony for Parkinson's Diagnosis | |
Li, Haolun1; Pun, Chi Man2; Xu, Feng3; Pan, Longsheng4; Zong, Rui4; Gao, Hao1; Lu, Huimin5 | |
2021-08-01 | |
Source Publication | ACM Transactions on Internet Technology |
ISSN | 1533-5399 |
Volume | 21Issue:3Pages:3397161 |
Abstract | Parkinson's disease is a neurodegenerative disease that affects millions of people around the world and cannot be cured fundamentally. Automatic identification of early Parkinson's disease on feature data sets is one of the most challenging medical tasks today. Many features in these datasets are useless or suffering from problems like noise, which affect the learning process and increase the computational burden. To ensure the optimal classification performance, this article proposes a hybrid feature selection algorithm based on an improved discrete artificial bee colony algorithm to improve the efficiency of feature selection. The algorithm combines the advantages of filters and wrappers to eliminate most of the uncorrelated or noisy features and determine the optimal subset of features. In the filter, three different variable ranking methods are employed to pre-rank the candidate features, then the population of artificial bee colony is initialized based on the significance degree of the re-rank features. In the wrapper part, the artificial bee colony algorithm evaluates individuals (feature subsets) based on the classification accuracy of the classifier to achieve the optimal feature subset. In addition, for the first time, we introduce a strategy that can automatically select the best classifier in the search framework more quickly. By comparing with several publicly available datasets, the proposed method achieves better performance than other state-of-the-art algorithms and can extract fewer effective features. |
Keyword | Artificial Bee Colony Algorithm Feature Extraction Machine Learning Parkinson's Disease |
DOI | 10.1145/3397161 |
URL | View the original |
Indexed By | SCIE |
Language | 英語English |
WOS Research Area | Computer Science |
WOS Subject | Computer Science, Information Systems ; Computer Science, Software Engineering |
WOS ID | WOS:000713626400011 |
Publisher | ASSOC COMPUTING MACHINERY, 1601 Broadway, 10th Floor, NEW YORK, NY 10019-7434 |
Scopus ID | 2-s2.0-85099891928 |
Fulltext Access | |
Citation statistics | |
Document Type | Journal article |
Collection | Faculty of Science and Technology DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE |
Corresponding Author | Gao, Hao; Lu, Huimin |
Affiliation | 1.College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Qixia District, No. 9Wenyuan Road, 210023, China 2.Department of Computer and Information Science, University of Macau, Taipa, Avenida da Universidade, 999078, Macao 3.School of Software, Tsinghua University, Haidian District, No. 30 Shuangqing Road, 100084, China 4.Department of Neurosurgery, PLA General Hospital, Haidian District, No. 28 Fuxing Road, 100853, China 5.Department of Mechanical and Control Engineering, Kyushu Institute of Technology, Hutian District, No. 1-1 Xianshui Road, 8048550, Japan |
Recommended Citation GB/T 7714 | Li, Haolun,Pun, Chi Man,Xu, Feng,et al. A Hybrid Feature Selection Algorithm Based on a Discrete Artificial Bee Colony for Parkinson's Diagnosis[J]. ACM Transactions on Internet Technology, 2021, 21(3), 3397161. |
APA | Li, Haolun., Pun, Chi Man., Xu, Feng., Pan, Longsheng., Zong, Rui., Gao, Hao., & Lu, Huimin (2021). A Hybrid Feature Selection Algorithm Based on a Discrete Artificial Bee Colony for Parkinson's Diagnosis. ACM Transactions on Internet Technology, 21(3), 3397161. |
MLA | Li, Haolun,et al."A Hybrid Feature Selection Algorithm Based on a Discrete Artificial Bee Colony for Parkinson's Diagnosis".ACM Transactions on Internet Technology 21.3(2021):3397161. |
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