An improved instance weighted linear regression

作者：Li Chaoqun; Li Hongwei^*

来源：Journal of Convergence Information Technology, 2010, 5(3): 122-128.

• Benchmark datasets
• Data mining applications
• Instance weighted linear regression
• Iteration
• Linear relation
• Model trees
• Number of iterations
• Regression model
• Scale-up
• Time complexity

Linear regression is a very simple regression model, However, the linear relation made by it is not realistic in many data mining application domains. Locally weighted linear regression and Model trees both combine locally learning and linear regression to improve linear regression. Our previous work called instance weighted linear regression is designed to improve the accuracy of linear regression without incuring the high time complexity confronting locally weighted linear regression and the tree learning suffering model trees. In order to get better weights for training instances and scale up the accuracy of instance weighted linear regression, we present an improved instance weighted linear regression in this pape. We simply denote it IIWLR. In IIWLR, the weight of each training instance is updated several times by applying the iterative method. The experimental results on 36 benchmark datasets show that IIWLR significantly outperforms instance weighted linear regression and is not sensitive to the number of iterations as long as it is not too small.

• 出版日期2010