计算机应用与软件2018，Vol.35Issue(1)：276-280,303,6.DOI:10.3969/j.issn.1000-386x.2018.01.048
不平衡数据分类预测的阈值优化算法ε-KSVM
THRESHOLD OPTIMIZATION ALGORITHM ε-KSVM FOR UNBALANCED DATA CLASSIFICATION PREDICTION
摘要
Abstract
In the era of Big Data,the classification data of electricity,medical and other industries with multidimensional mass characteristics are often unbalanced data,a small number of samples of the classification is often wrong.According to different datasets,the distribution tendency of datasets may affect the accuracy of classifiers.The traditional Classifier KSVM adds 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 ε-KSVM classifier with thresholds of dynamic adjustment for different datasets so that the misclassification information is reduced.The experimental results showed that the prediction accuracy was improved greatly.关键词
支持向量机/K近邻算法/阈值/非平衡数据/遗传算法Key words
SVM/KNN/Threshold/Imbalanced datasets/Genetic algorithm分类
信息技术与安全科学引用本文复制引用
金鑫,葛国青,陆旭,赵永彬..不平衡数据分类预测的阈值优化算法ε-KSVM[J].计算机应用与软件,2018,35(1):276-280,303,6.基金项目
国网科技部项目(SGTYHT/14-JS-188). （SGTYHT/14-JS-188）
