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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 PublicationACM Transactions on Internet Technology
ISSN1533-5399
Volume21Issue: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.

KeywordArtificial Bee Colony Algorithm Feature Extraction Machine Learning Parkinson's Disease
DOI10.1145/3397161
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science
WOS SubjectComputer Science, Information Systems ; Computer Science, Software Engineering
WOS IDWOS:000713626400011
PublisherASSOC COMPUTING MACHINERY, 1601 Broadway, 10th Floor, NEW YORK, NY 10019-7434
Scopus ID2-s2.0-85099891928
Fulltext Access
Citation statistics
Document TypeJournal article
CollectionFaculty of Science and Technology
DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorGao, Hao; Lu, Huimin
Affiliation1.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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