The AdaBoost Algorithm with Prior Probabilities and the Visualization Demonstrated in GIS for Geo-hazard Forecasting

摘要：

The AdaBoost integration learning algorithm is based on the idea of promoting the classification precision through certain combinations by a number of classifiers. This paper puts forward the AdaBoost algorithm with prior probabilities. Each classifier which is used for the combination is usually obtained through the sample collection by certain training. Using the sample to centralize the ratio of different kinds of goals can reflect various classifiers prior probability. Using this parameter, we can make good use of AdaBoost algorithm to predict hazard quickly and will not cause the phenomenon of overstudying. Based on the classification problem of two-classes, experiments with UCI datasets show the validity of the AdaBoost algorithm with prior probabilities. The performance of the AdaBoost algorithm with prior probabilities is better than the traditional AdaBoost algorithm. The AdaBoost algorithm with prior probabilities is confirmed to give better prediction in Geo-hazard risk modeling through the visualization demonstrated in GIS.

关键词： Machine learning Boosting AdaBoost prior probability weak learning theory

作者: Zhao Xiang-hui Fu Zhong-liang Yao Yu Miao Qing

作者单位: Chengdu Computer Applications Institute, Chinese Academy of Sciences Chengdu, 610041 P.R. of China Graduate University of Chinese Academy of Sciences Beijing, 100049 P.R. of China

会议类型: 国际会议

会议名称: 2009 Ninth International Conference on Hybrid Intelligent Systems(第九届混合智能系统国际会议 HIS 2009)

会议地点: 沈阳

会议语种:英文

页码: 1-6

在线出版日期: 2009-08-12（万方平台首次上网日期，不代表论文的发表时间）