本文已被：浏览 1550次 下载 1861次
Received:November 09, 2017 Revised:November 29, 2017
Received:November 09, 2017 Revised:November 29, 2017
中文摘要: 对不平衡数据集SVM分类存在着分类结果偏向多数类的情况，使得分类结果中少数类的F1-Measure值偏低.本文提出一种不改变样本集合的样本数，并结合样本点总数，分类过程中的支持向量个数，少数类和多数类的准确率，生成权重值对分类超平面参数b进行优化，以此提高少数类样本点分类准确率的方法，并通过实验证明该方法的有效性.
Abstract:SVM classification result on imbalance data set is partial to majority class. It makes the F1_measure value of minority class inadequate. This paper presents a method which improves the classification accuracy of minority class. The method generates weights to optimize parameters b of the classification hyper plane without changing the number of samples, and combines the total number of samples, the number of support vector, the accuracy of minority class and majority class. Finally, the effectiveness of the method is proved by experiments.
文章编号： 中图分类号： 文献标志码：
基金项目:
| Author Name | Affiliation | |
| YAN Xiao-Ming | College of Mathematics and Informatics, Fujian Normal University, Fuzhou 350117, China | yanxm@fjnu.edu.cn |
| Author Name | Affiliation | |
| YAN Xiao-Ming | College of Mathematics and Informatics, Fujian Normal University, Fuzhou 350117, China | yanxm@fjnu.edu.cn |
引用文本：
严晓明.不平衡数据集中分类超平面参数优化方法.计算机系统应用,2018,27(7):219-223
YAN Xiao-Ming.Optimization Method of Classification Hyperplane Parameter under Imbalance Data Set.COMPUTER SYSTEMS APPLICATIONS,2018,27(7):219-223
严晓明.不平衡数据集中分类超平面参数优化方法.计算机系统应用,2018,27(7):219-223
YAN Xiao-Ming.Optimization Method of Classification Hyperplane Parameter under Imbalance Data Set.COMPUTER SYSTEMS APPLICATIONS,2018,27(7):219-223
