کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
387325 | 660900 | 2012 | 5 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: A novel approach to estimate proximity in a random forest: An exploratory study A novel approach to estimate proximity in a random forest: An exploratory study](/preview/png/387325.png)
A data proximity matrix is an important information source in random forests (RF) based data mining, including data clustering, visualization, outlier detection, substitution of missing values, and finding mislabeled data samples. A novel approach to estimate proximity is proposed in this work. The approach is based on measuring distance between two terminal nodes in a decision tree. To assess the consistency (quality) of data proximity estimate, we suggest using the proximity matrix as a kernel matrix in a support vector machine (SVM), under the assumption that a matrix of higher quality leads to higher classification accuracy. It is experimentally shown that the proposed approach improves the proximity estimate, especially when RF is made of a small number of trees. It is also demonstrated that, for some tasks, an SVM exploiting the suggested proximity matrix based kernel, outperforms an SVM based on a standard radial basis function kernel and the standard proximity matrix based kernel.
Journal: Expert Systems with Applications - Volume 39, Issue 17, 1 December 2012, Pages 13046–13050