Improving the classification performance of biological imbalanced datasets by swarm optimization algorithms
Jinyan Li1; Simon Fong1; Sabah Mohammed2; Jinan Fiaidhi2
2016-10
Source PublicationJournal of Supercomputing
ISSN0920-8542
Volume72Issue:10Pages:3708–3728
Abstract

Classification which is a popular supervised machine learning method has many applications in computational biology, where data samples are automatically categorized into predefined labels with the aid of data mining. Often the training samples contain very few instances of interest (e.g., medical anomalies, rare disease in a population, and unusual syndromes, etc.), but many normal instances. Such imbalanced ratio of data distributions among the target labels hampers the efficacy of classification algorithms, because the induced model has not been trained with sufficient amount of instances of the interesting label(s), but overwhelmed with ordinary training records. Traditional remedies attempt to rebalance the data distributions of the target classes, by inflating the interesting instances artificially, reducing the majority of the common instances or a combination of both. Though the fundamental concept is effective, there is no clear guideline on how to strike a balance between fabricating the rare samples and reducing the norms, with the purpose of maximizing the classification accuracy. In this paper, an optimization model using different swarm strategies (Bat-inspired algorithm and PSO) is proposed for adaptively balancing the increase/decrease of the class distribution, depending on the properties of the biological datasets. The optimization is extended for achieving the highest possible accuracy and Kappa statistics at the same time as well. The optimization model is tested on five imbalanced medical datasets, which are sourced from lung surgery logs and virtual screening of bioassay data. Computer simulation results show that the proposed optimization model outperforms other class balancing methods in medical data classification. 

KeywordImbalanced Biological Data Medical Classification Parameter Optimization Swarm Algorithm
DOIhttps://doi.org/10.1007/s11227-015-1541-6
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000385417400004
PublisherSPRINGER, VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
The Source to ArticleScopus
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Citation statistics
Cited Times [WOS]:16   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorJinyan Li; Simon Fong; Sabah Mohammed; Jinan Fiaidhi
Affiliation1.Department of Computer and Information Science, University of Macau, Taipa, Macau SAR
2.Department of Computer Science, Lakehead University, Taipa, Macau SAR
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Jinyan Li,Simon Fong,Sabah Mohammed,et al. Improving the classification performance of biological imbalanced datasets by swarm optimization algorithms[J]. Journal of Supercomputing,2016,72(10):3708–3728.
APA Jinyan Li,Simon Fong,Sabah Mohammed,&Jinan Fiaidhi.(2016).Improving the classification performance of biological imbalanced datasets by swarm optimization algorithms.Journal of Supercomputing,72(10),3708–3728.
MLA Jinyan Li,et al."Improving the classification performance of biological imbalanced datasets by swarm optimization algorithms".Journal of Supercomputing 72.10(2016):3708–3728.
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