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Solving the Under-Fitting Problem for Decision Tree Algorithms by Incremental Swarm Optimization in Rare-Event Healthcare Classification
Li, Jinyan1; Fong, Simon1; Mohammed, Sabah2; Fiaidhi, Jinan2; Chen, Qian3; Tan, Zhen3
2016-08-01
Source PublicationJournal of Medical Imaging and Health Informatics
ISSN2156-7018
Volume6Issue:4Pages:1102-1110
Abstract

Healthcare data are well-known to be imbalanced in the data distribution of target classes where the samples of interest are much fewer than the ordinary samples. When it comes to healthcare data classification, insufficient supervised training in decision tree induction is prone to happen, leading to poor classification/prediction accuracy. Swarm Balancing Algorithm (SBA) was proposed to optimize the parameter values of a popular datarebalancing method called Synthetic Minority Over-sampling Technique (SMOTE) for rectifying the under-fitting problems. Though it works well, the drawback of SBA is the requirement that all the data must be initially available. In this paper, an alternative approach which extends from SBA, namely, Incremental Swarm Balancing Algorithm (ISBA) is investigated on the impacts of decision trees. ISBA obtains higher classification accuracy at faster speed than SBA by optimizing SMOTE and training a decision tree on the fly. In our design, two swarm algorithms, particle swarm optimization and bat-inspired algorithm, are used to couple with two different types of decision tree classifiers, Decision Tree (DT) and Hoeffding Tree (HT). The former represents the traditional batch-type decision tree model, and the latter is typical incremental decision tree model. Experimentation over two sets of imbalanced healthcare data is performed, with the aim of comparing and contrasting the efficacy of ISBA for DT and HT.

KeywordDecision Tree Imbalanced Healthcare Dataset Incremental Swarm Optimization Swarm Intelligence Algorithm
DOIhttps://doi.org/10.1166/jmihi.2016.1807
URLView the original
Indexed BySCI
Language英语
WOS Research AreaMathematical & Computational Biology ; Radiology, Nuclear Medicine & Medical Imaging
WOS SubjectMathematical & Computational Biology ; Radiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000386493900029
PublisherAMER SCIENTIFIC PUBLISHERS, 26650 THE OLD RD, STE 208, VALENCIA, CA 91381-0751 USA
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Citation statistics
Cited Times [WOS]:6   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.Univ Macau, Dept Comp & Informat Sci, Macau, Peoples R China
2.Lakehead Univ, Dept Comp Sci, Thunder Bay, ON P7B 5E1, Canada
3.Shenzhen Children Hosp, Dept Pediat Neurosurg, Shenzhen 518026, Peoples R China
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Li, Jinyan,Fong, Simon,Mohammed, Sabah,et al. Solving the Under-Fitting Problem for Decision Tree Algorithms by Incremental Swarm Optimization in Rare-Event Healthcare Classification[J]. Journal of Medical Imaging and Health Informatics,2016,6(4):1102-1110.
APA Li, Jinyan,Fong, Simon,Mohammed, Sabah,Fiaidhi, Jinan,Chen, Qian,&Tan, Zhen.(2016).Solving the Under-Fitting Problem for Decision Tree Algorithms by Incremental Swarm Optimization in Rare-Event Healthcare Classification.Journal of Medical Imaging and Health Informatics,6(4),1102-1110.
MLA Li, Jinyan,et al."Solving the Under-Fitting Problem for Decision Tree Algorithms by Incremental Swarm Optimization in Rare-Event Healthcare Classification".Journal of Medical Imaging and Health Informatics 6.4(2016):1102-1110.
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