Imbalanced data classification algorithm with support vector machine kernel extensions

Learning from the imbalanced data samples so as to achieve accurate classification is an important research content in data mining field. It is very difficult for classification algorithm to achieve a higher accuracy because the uneven distribution of data samples makes some categories have few samp...

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Bibliographic Details
Published in:Evolutionary intelligence 2019-09, Vol.12 (3), p.341-347
Main Authors: Wang, Feng, Liu, Shaojiang, Ni, Weichuan, Xu, Zhiming, Qiu, Zemin, Wan, Zhiping, Pan, Zhihong
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Applications of Mathematics
Artificial Intelligence
Bioinformatics
Chi-square test
Classification
Computer simulation
Control
Data mining
Datasets
Economic models
Engineering
Kernel functions
Machine learning
Mathematical and Computational Engineering
Mechatronics
Robotics
Special Issue
Statistical Physics and Dynamical Systems
Support vector machines
Training
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Summary:Learning from the imbalanced data samples so as to achieve accurate classification is an important research content in data mining field. It is very difficult for classification algorithm to achieve a higher accuracy because the uneven distribution of data samples makes some categories have few samples. A imbalanced data classification algorithm of support vector machines (KE-SVM) is proposed in this article, this algorithm achieve the initial classification of data samples by training the maximum margin classification SVM model, and then obtaining a new kernel extension function. based on Chi square test and weight coefficient calculation, through training the samples again by the new vector machine with kernel function to improve the classification accuracy. Through the simulation experiments of real data sets of artificial data set, it shows that the proposed method has higher classification accuracy and faster convergence for the uneven distribution data.
ISSN:1864-5909
1864-5917
DOI:10.1007/s12065-018-0182-0