On pruning and feature engineering in Random Forests.

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.

Name dc:type.qualificationname

PhD

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

Robert Gordon University

Year dc:date.issued

2016

Author dc:creator

• Fawagreh, Khaled

Advisor dc:contributor.advisor

• Mohamed Medhat Gaber and Eyad Elyan

• Random Forests
• Ensemble classification
• Clustering
• Local outlier factor
• Replicator dynamics

Language dc:language

en

Identifier

oai:rgu-repository.worktribe.com:248863

OAI identifier oai:identifier

oai:rgu-repository.worktribe.com:248863