Proceedings of the 2017 2nd International Conference on Modelling, Simulation and Applied Mathematics (MSAM2017)

Threshold Optimization Algorithm -KSVM for Unbalanced Data Classification Prediction

Authors
Guoqing Ge, Xin Jin, Xu Lu, Yongbin Zhao
Corresponding Author
Guoqing Ge
Available Online March 2017.
DOI
https://doi.org/10.2991/msam-17.2017.25How to use a DOI?
Keywords
SVM; KNN; threshold; imbalanced datasets; Genetic Algorithm
Abstract
In the era of Big Data, the multidimensional datasets from electric power, medical treatment and other industries are often unbalanced, and the positive data usually costs more seriously when being classified. According to different datasets, the distribution tendency of datasets may reduce the accuracy of classifiers. Based on SVM, the traditional Classifier KSVM introduces KNN algorithm effectively, that increases the effective classification information for error-prone points near the hyperplane, but at the same time it introduces more noise. Based on the defect that the KSVM algorithm with fixed threshold applied to unbalanced datasets, this paper proposes an improved -KSVM classifier with thresholds of dynamic adjustment for different datasets. The classifier applies Genetic Algorithm to adjust the boundary, namely the threshold dynamically so that the misclassification information is reduced. The experimental results show that the prediction accuracy is improved greatly.
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Proceedings
2017 2nd International Conference on Modelling, Simulation and Applied Mathematics (MSAM2017)
Part of series
Advances in Intelligent Systems Research
Publication Date
March 2017
ISBN
978-94-6252-324-1
ISSN
1951-6851
DOI
https://doi.org/10.2991/msam-17.2017.25How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Guoqing Ge
AU  - Xin Jin
AU  - Xu Lu
AU  - Yongbin Zhao
PY  - 2017/03
DA  - 2017/03
TI  - Threshold Optimization Algorithm -KSVM for Unbalanced Data Classification Prediction
BT  - 2017 2nd International Conference on Modelling, Simulation and Applied Mathematics (MSAM2017)
PB  - Atlantis Press
SP  - 109
EP  - 113
SN  - 1951-6851
UR  - https://doi.org/10.2991/msam-17.2017.25
DO  - https://doi.org/10.2991/msam-17.2017.25
ID  - Ge2017/03
ER  -