计算机应用与软件Issue(1)：186-190,5.DOI:10.3969/j.issn.1000-386x.2014.01.049
基于惩罚的S VM和集成学习的非平衡数据分类算法研究
RESEARCH ON CLASSIFYING UNBALANCED DATA BASED ON PENALTY-BASED SVM AND ENSEMBLE LEARNING
刘进军1
作者信息
- 1. 阳江职业技术学院计算机科学系 广东 阳江529500
- 折叠
摘要
Abstract
To process the unbalanced data with various algorithms has become a focus in data mining research.Aiming at the characteristic of the unbalanced data,on the basis of studying the related theory of support vector machines and the K-SVM algorithm,we present the penalty mechanism-based PFKSVM (SVM based on penalty factor ) method to overcome the problem of K-SVM that it is prone to misclassification when nearby the optimal classification surface.Then,we propose an ensemble learning model composing of the reconstructed sampling layer,basic training layer and decision layer.The experiment using UCI public data sets verifies the predominance of PFKSVM algorithm and the ensemble model in processing the unbalanced data classification.关键词
数据挖掘/支持向量机(SVM)/非平衡数据分类/集成学习Key words
Data mining/Support vector machine(SVM)/Unbalanced data classification/Ensemble learning分类
信息技术与安全科学引用本文复制引用
刘进军..基于惩罚的S VM和集成学习的非平衡数据分类算法研究[J].计算机应用与软件,2014,(1):186-190,5.
