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

Authors:
C. Englund;A. Verikas
Affiliations:
Viktoria Institute, Lindholmspiren 3A, S 417 56 Göteborg, Sweden;Intelligent Systems Laboratory, Halmstad University, Box 823, S 301 18 Halmstad, Sweden and Department of Electrical & Control Equipment, Kaunas University of Technology Studentu 50, LT-51368 Kaun ...
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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Abstract

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.