Signal Prediction Based on Boosting and Decision Stump
Signal prediction has attracted more and more attention from data mining and machine learning communities.Decision stump is a one-level decision tree,and it classifies instances by sorting them based on feature values.The boosting is a kind of powerful ensemble methods and can improves the performance of prediction significantly.In this paper,boosting and decision stump algorithm are combined to analyze and predict the signal data.An experimental evaluation is carried out on the public signal dataset and the experimental results show that the boosting and decision stump based algorithm improves performance of signal prediction obviously.
decision stump boosting signal prediction
Lei Shi Hongbo Qiao Xinming Ma
College of Information and Management Science,HeNan Agricultural University,Zhengzhou 450002 China
国内会议
第10届全国计算机支持的协同工作学术会议暨中国计算机学会协同计算专委年度工作会议
太原
英文
544-550
2015-08-28（万方平台首次上网日期，不代表论文的发表时间）