Adaptive Ensemble Method Based on Spatial Characteristics for Classifying Imbalanced Data
Joint Authors
Gui, Guan
Wang, Lei
Zhao, Lei
Zheng, Baoyu
Huang, Ruochen
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-12-26
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
The class imbalance problems often reduce the classification performance of the majority of standard classifiers.
Many methods have been developed to solve these problems, such as cost-sensitive learning methods, synthetic minority oversampling technique (SMOTE), and random oversampling (ROS).
However, the existing methods still have some problems due to the possible performance loss of useful information and overfitting.
To solve the problems, we propose an adaptive ensemble method by using the most advanced feature of self-adaption by considering an average Euclidean distance between test data and training data, where the average distance is calculated by k-nearest neighbors (KNN) algorithm.
Simulation results are provided to confirm that the proposed method has a better performance than existing ensemble methods.
American Psychological Association (APA)
Wang, Lei& Zhao, Lei& Gui, Guan& Zheng, Baoyu& Huang, Ruochen. 2017. Adaptive Ensemble Method Based on Spatial Characteristics for Classifying Imbalanced Data. Scientific Programming،Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1203390
Modern Language Association (MLA)
Wang, Lei…[et al.]. Adaptive Ensemble Method Based on Spatial Characteristics for Classifying Imbalanced Data. Scientific Programming No. 2017 (2017), pp.1-8.
https://search.emarefa.net/detail/BIM-1203390
American Medical Association (AMA)
Wang, Lei& Zhao, Lei& Gui, Guan& Zheng, Baoyu& Huang, Ruochen. Adaptive Ensemble Method Based on Spatial Characteristics for Classifying Imbalanced Data. Scientific Programming. 2017. Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1203390
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1203390