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摘要:
Traditional classification algorithms perform not very well on imbalanced data sets and small sample size. To deal with the problem, a novel method is proposed to change the class distribution through adding virtual samples, which are generated by the windowed regression over-sampling (WRO) method. The proposed method WRO not only reflects the additive effects but also reflects the multiplicative effect between samples. A comparative study between the proposed method and other over-sampling methods such as synthetic minority over-sampling technique (SMOTE) and borderline over-sampling (BOS) on UCI datasets and Fourier transform infrared spectroscopy (FTIR) data set is provided. Experimental results show that the WRO method can achieve better performance than other methods.
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篇名 An Improved Algorithm for Imbalanced Data and Small Sample Size Classification
来源期刊 数据分析和信息处理(英文) 学科 医学
关键词 Class IMBALANCE Learning OVER-SAMPLING HIGH-DIMENSIONAL Small-Sample Size SUPPORT VECTOR Machine
年,卷(期) 2015,(3) 所属期刊栏目
研究方向 页码范围 27-33
页数 7页 分类号 R73
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Class
IMBALANCE
Learning
OVER-SAMPLING
HIGH-DIMENSIONAL
Small-Sample
Size
SUPPORT
VECTOR
Machine
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相关学者/机构
期刊影响力
数据分析和信息处理(英文)
季刊
2327-7211
武汉市江夏区汤逊湖北路38号光谷总部空间
出版文献量(篇)
106
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0
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