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Title: On pruning and feature engineering in Random Forests
Author: Fawagreh, Khaled
ISNI:       0000 0004 6056 7826
Awarding Body: Robert Gordon University
Current Institution: Robert Gordon University
Date of Award: 2016
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Random Forest (RF) is an ensemble classification technique that was developed by Leo 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 optimizing RF further by enhancing and improving its performance accuracy. This explains why there have been many extensions of RF where each extension employed a variety of techniques and strategies to improve certain aspect(s) of RF. The main focus of this dissertation is to develop new extensions of RF using new optimization techniques that, to the best of our knowledge, have never been used before to optimize RF. These techniques are clustering, the local outlier factor, diversified weighted subspaces, and replicator dynamics. Applying these techniques on RF produced four extensions which we have termed CLUB-DRF, LOFB-DRF, DSB-RF, and RDB-DR respectively. Experimental studies on 15 real datasets showed favorable results, demonstrating the potential of the proposed methods. Performance-wise, CLUB-DRF is ranked first in terms of accuracy and classifcation speed making it ideal for real-time applications, and for machines/devices with limited memory and processing power.
Supervisor: Gaber, Mohamed Medhat ; Elyan, Eyad Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Random Forests ; Ensemble classification ; Clustering ; Local outlier factor ; Replicator dynamics