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Optimizing SMOTE by Metaheuristics with Neural Network and Decision Tree
Jinyan Li; Simon Fong; Yan Zhuang
2016-01-14
Conference Name2015 3rd International Symposium on Computational and Business Intelligence (ISCBI)
Source PublicationProceedings - 2015 3rd International Symposium on Computational and Business Intelligence, ISCBI 2015
Pages26-32
Conference Date7-9 Dec. 2015
Conference PlaceBali, Indonesia
PublisherIEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Abstract

SMOTE (Synthetic minority over-sampling technique) is a commonly used over-sampling technique to subside the imbalanced dataset problem. Traditionally SMOTE has two key important parameters, one is to control the amount of over-sampling, and the other specifies the area of the nearest neighbors. These two parameters are arbitrarily chosen by user. So there are no universally best default values. In this paper, we propose a method that uses metaheuristic optimization algorithms, Bat-inspired algorithm (BAT) and particle swarm optimization algorithm (PSO), to optimize the selection of these two parameters for improving the performance of classifiers for data mining imbalanced data. Users are allowed to define the minimum requirements for two performance indicators, such as Kappa and accuracy. The method iteratively searches for the best pair of SMOTE parameters. Two metaherustics, PSO and BAT are used to find the best parameter values for achieving the required performance via SMOTE. At the end, the highest possible accuracy is obtained while satisfying a minimum degree of Kappa as defined by the user. In comparison to the brute-force method, our method shows advantage in shorter run-time and good classification performance.

KeywordParameter Selection Optimization Smote Swarm Intelligence
DOIhttps://doi.org/10.1109/ISCBI.2015.12
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications ; Computer Science, Theory & Methods
WOS IDWOS:000374594000005
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Citation statistics
Cited Times [WOS]:5   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
AffiliationDepartment of Computer and Information Science, University of Macau, Taipa, Macau SAR
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Jinyan Li,Simon Fong,Yan Zhuang. Optimizing SMOTE by Metaheuristics with Neural Network and Decision Tree[C]:IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA,2016:26-32.
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