Bagging Ensemble of SVM Based on Negative Correlation Learning

摘要：

A new support vector machine (SVM) ensemble algorithm based on negative correlation learning is studied in this paper. This approach can produce individual SVMs whose errors tend to be negatively correlated, so the diversity is emphasized among individual SVMs in an ensemble. This method is applied in modeling of Leaching process of hydrometallurgy. The empirical results show that the method does consistently improve the prediction accuracy versus basic bagging algorithms and single SVM algorithms for leaching process.

关键词： component Ensemble Sagging Negative Correlation Learning Support Vector Machine Leaching Process

作者: Guanghao Hu Zhizhong Mao

作者单位: Liaoning Key Laboratory of Integrated Automation of Process Industry,MOE Northeastern University Shenyang,Liaoning,China

会议类型: 国际会议

会议名称: 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems(2009 IEEE 智能计算与智能系统国际会议)

会议地点: 上海

会议语种:英文

页码: 279-283

在线出版日期: 2009-11-20（万方平台首次上网日期，不代表论文的发表时间）