摘要
重采样技术在解决非平衡类分类问题上得到了广泛的应用。其中,Chawla提出的SMOTE(Synthetic Minority Oversampling Technique)算法在一定程度上缓解了数据的不平衡程度,但这种方法对少数类数据不加区分地进行过抽样,容易造成过拟合。针对此问题,本文提出了一种新的过采样方法:DS-SMOTE方法。DS-SMOTE算法基于样本的密度来识别稀疏样本,并将其作为采样过程中的种子样本;然后在采样过程中采用SMOTE算法的思想,在种子样本与其k近邻之间产生合成样本。实验结果显示,DS-SMOTE算法与其他同类方法相比,准确率以及G值有较大的提高,说明DS-SMOTE算法在处理非平衡数据分类问题上具有一定优势。
In recent years, over-sampling has been widely used in the field of classification of imbalanced classes. The SMOTE（Synthetic Minority Oversampling Technique） algorithm, presented by Chawla, alleviates the degree of data imbalance to a certain extent, but can lead to over-fitting. To solve this problem, this paper presents a new sampling method, DS-SMOTE, which identifies sparse samples based on their density and uses them as seed samples in the process of sampling. The SMOTE algorithm is then adopted, and a synthetic sample is generated between the seed sample and its k neighbor. The proposed algorithm showed great improvement in precision and G-mean compared with similar al- gorithms, and it has advantage of treating imbalanced data classification.
出处
《智能系统学报》
CSCD
北大核心
2017年第6期865-872,共8页
CAAI Transactions on Intelligent Systems
基金
国家自然科学基金项目(61772323
61402272)
山西省自然科学基金项目(201701D121051)
关键词
非平衡
分类
采样
准确率
密度
imbalance
classification
sampling
precision
density
