An outlier ranking tree selection approach to extreme pruning of random forests.

Fawagreh, Khaled; Gaber, Mohamed Medhat; Elyan, Eyad

Abstract

Random Forest (RF) is an ensemble classification technique that was developed by Breiman over a decade ago. Compared with other ensemble techniques, it has proved its accuracy and superiority. Many researchers, however, believe that there is still room for enhancing and improving its performance in terms of predictive accuracy. This explains why, over the past decade, there have been many extensions of RF where each extension employed a variety of techniques and strategies to improve certain aspect(s) of RF. Since it has been proven empirically that ensembles tend to yield better results when there is a significant diversity among the constituent models, the objective of this paper is twofold. First, it investigates how an unsupervised learning technique, namely, Local Outlier Factor (LOF) can be used to identify diverse trees in the RF. Second, trees with the highest LOF scores are then used to create a new RF termed LOFB-DRF that is much smaller in size than RF, and yet performs at least as good as RF, but mostly exhibits higher performance in terms of accuracy. The latter refers to a known technique called ensemble pruning. Experimental results on 10 real datasets prove the superiority of our proposed method over the traditional RF. Unprecedented pruning levels reaching as high as 99% have been achieved at the time of boosting the predictive accuracy of the ensemble. The notably extreme pruning level makes the technique a good candidate for real-time applications.

Citation

FAWAGREH, K., GABER, M.M. and ELYAN, E. 2016. An outlier ranking tree selection approach to extreme pruning of random forests. In Jayne, C. and Iliadis, L. (eds.) Engineering applications of neural networks: proceedings of the 17th International engineering applications of neural networks conference (EANN 2016), 2-5 September 2016, Aberdeen, UK. Communications in computer and information science, 629. Cham: Springer [online], pages 267-282. Available from: https://doi.org/10.1007/978-3-319-44188-7_20

• Conference Name: 17th International engineering applications of neural networks conference (EANN 2016)
• Conference Location: Aberdeen, UK
• Start Date: Sep 2, 2016
• End Date: Sep 5, 2016
• Acceptance Date: Jun 5, 2016
• Online Publication Date: Aug 19, 2016
• Publication Date: Sep 30, 2016
• Deposit Date: Jul 4, 2017
• Publicly Available Date: Jul 4, 2017
• Print ISSN: 1865-0929
• Publisher: Springer
• Volume: 629
• Pages: 267-282
• Series Title: Communications in computer and information science
• Series Number: 629
• Series ISSN: 1865-0929
• ISBN: 9783319441870
• DOI: https://doi.org/10.1007/978-3-319-44188-7_20
• Keywords: Random forest (RF); Diversity; Classifiers; Estimators; Ensembles
• Public URL: http://hdl.handle.net/10059/2398