Attribute reduction-based boosting and its application
Xiaodong Liu · Journal of Shenzhen Institute of Information Technology · 2006
Boosting is one of the most important methods in the field of data mining,which can improve the predic- tion accuracy of the algorithm.However,it often results in overfitting,namely,the deterioration of model's extrapo- lation due to high accuracy.One of the causes of the overfitting in boosting is the static voting strategy.In this paper. an application of neural net on attribute reduction is proposed,by which,to a greater extent,the overfitting in boost- ing is avoided.We made an analysis of client lost based on the telecom database of a city in Hunan Province,which shows that this application effectively improves the accuracy of models and the comprehensibility of results.