An Improved Algorithm for Imbalanced Data and Small Sample Size Classification

• 作者: Boping Li Chen Dong Dongfa Guo Guifang Liu Jing Tan Qiuhong Huang Qiwei Xie Shengkai Xie Yong Hu Zengwei Fan
• 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）: sjfxhxxclyw_2015,（3）
• 页码范围: 27-33
• 页数: 7页
• 分类号: R73

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.