摘要
有1份仅含A类与B类的训练集,与1份包含不止这2个类别的测试集,如何对测试集中的样本进行分类?针对这个问题,本文提出3种基于SVM方法和最小包围球方法(minimum enclosing ball,MEB)的新类别分类方法。这3种新类别分类方法不仅解决了SVM不能正确判别新类别的缺点,而且在实际数据分析中获得了较好的效果。本文使用乳腺癌分子分型数据进行分析,最终样本分类准确率可达90%以上,新类别样本分类正确率可达99%以上。
This paper mainly studies the following problems:if there is a training set containing only A and B classes,and a test set containing more than these two categories,how should the samples in the test set be classified?For this problem,three new category classification methods based on SVM and minimum enclosing ball method are proposed.These three new methods not only can solves the weakness of SVM that can't correctly identifying new categories,but also can obtain good effect in the real data analysis.The data set used in this paper is breast cancer molecular subtype data set.The final sample classification accuracy rate can reach more than 90%,and the classification accuracy of the new category samples can be more than 99%.
作者
杨迪
方扬鑫
周彦
YANG Di;FANG Yangxin;ZHOU Yan(College of Mathematics and Statistics,Shenzhen University,Shenzhen Guangdong 518060,China)
出处
《广西师范大学学报（自然科学版）》
CAS
北大核心
2022年第1期57-67,共11页
Journal of Guangxi Normal University:Natural Science Edition
基金
国家自然科学基金(12071305,11871390,11871411)
广东省自然科学基金(2020B1515310008)。
关键词
机器学习
多分类问题
支持向量机
MEB
SVDD
machine learning
multi-classification problem
support vector machine
MEB
SVDD
