A novel approach to estimate proximity in a random forest: An exploratory study

作者：Englund C^*; Verikas A

来源：Expert Systems with Applications, 2012, 39(17): 13046-13050.

DOI：10.1016/j.eswa.2012.05.094

• Random forest
• Proximity matrix
• Support vector machine
• Kernel matrix
• Data mining

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.

• 出版日期2012-12-1