HOW EFFICIENT IS SUPPORT VECTOR MACHINE: A COMPARATIVE ANALYSIS

摘要：

In this paper, efficiency of Support Vector Machine (SVM) and Artificial Neural Network (ANN) is analyzed using unbalanced dataset.The dataset analyzed in this study is obtained from COIL Challenge2000 and it is highly unbalanced with 94％ good customers data and 6％ bad or fraud customers data.We employed balancing techniques and SMOTE to bring the balance in the data and analysis is carried out.We employed (1) Under-sampling, (2) Over-sampling and (3) Synthetic Minority Oversampling Technique (SMOTE) for balancing the dataset.Since identifying fraudulent cases is paramount from the business perspective, management accords higher priority on sensitivity only.Therefore considering sensitivity alone, we observed that SVM outperformed with original unbalanced data.It is also observed that NN performed better with balanced data compared to it performance using unbalanced data.

关键词： SVM ANN Unbalanced Data Balancing Techniques SMOTE

作者: M.A.H.FARQUAD S.M.BASHA MD.ANWAR ALI JABEEN SULTANA S.HARIKRISHNA

作者单位: Research and Development Cell,Department Of Computer Science Engineering,Al-Habeeb College Of Engine Vivekananda Institute of Technology and Science - School of Engineering and Technology Karimnagar,A.

会议类型: 国际会议

会议名称: 2011 3rd International Conference on Computer Technology and Development(2011第三届计算机技术与发展国际会议 ICCTD2011)

会议地点: 成都

会议语种:英文

页码: 323-327

在线出版日期: 2011-11-25（万方平台首次上网日期，不代表论文的发表时间）