Distribution-Sensitive Unbalanced Data Oversampling Method for Medical Diagnosis.
Aiming at the problem of low accuracy of classification learning algorithm caused by serious imbalance of sample set in medical diagnostic application, this paper proposes a distribution-sensitive oversampling algorithm for imbalanced data. The algorithm accurately divides the minority samples into...
| Publicado en: | Journal of Medical Systems Vol. 43; no. 2; pp. 1 - 2 |
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| Autores principales: | , , , |
| Formato: | algorithm research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=134561919&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 134561919 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Feb2019 vid: 43 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 134561919 134561919 134561919 10.1007/s10916-018-1154-8 134561919 ppf: 1 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Distribution-Sensitive Unbalanced Data Oversampling Method for Medical Diagnosis. aug: au: Han, Weihong Huang, Zizhong Li, Shudong Jia, Yan affil: Institute of Advanced Technology in Cyberspace, Guangzhou University, 510006, Guangzhou, Guangdong, China sug: subj: Disease Diagnosis Disease Classification Diagnosis, Computer Assisted Data Analysis Algorithms Evaluation Sampling Methods Validity Human Decision Support Systems, Clinical Data Collection Methods Funding Source Data Analytics ab: Aiming at the problem of low accuracy of classification learning algorithm caused by serious imbalance of sample set in medical diagnostic application, this paper proposes a distribution-sensitive oversampling algorithm for imbalanced data. The algorithm accurately divides the minority samples into noise samples, unstable samples, boundary samples and stable samples according to the location of the minority samples. Different samples are processed differently to select the most suitable sample for the synthesis of new samples. In the case of sample synthesis, a distribution-sensitive sample synthesis method is adopted. Different sample synthesis methods are selected according to their different distance from the surrounding minority samples, so as to ensure that the newly synthesized samples have the same characteristics with the original minority samples. The real medical diagnostic data test shows that this algorithm improves the accuracy rate of classification learning algorithm compared with the existing sampling algorithms, especially for the accuracy rate and recall rate of minority classes. pubtype: Academic Journal doctype: algorithm research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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