Investigations on the Characteristics of Random Decision Tree Ensembles

Richards, G. and Wang, W. (2006) Investigations on the Characteristics of Random Decision Tree Ensembles. In: IEEE Proceedings of the International Joint Conference on Neural Networks (IJCNN '06), 2006-07-16 - 2006-07-21.

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Abstract

An ensemble is viewed as a machine learning system that combines multiple models to work collectively in the hope of producing a better performance than that of individuals. However, an ensemble's accuracy cannot be easily determined as it involves several factors, e.g. individual model's accuracy, diversity between its member models, decision- making strategy and number of members and the relationships between them are unclear. This paper, taking random decision tree ensembles as testing platforms, investigates these relationships and the strategies for creating ensembles from randomly generated trees. Specifically, we devised three sets of procedures for conducting experiments using twelve data sets from the UCI repository to determine the importance of individual model accuracy and the diversity between decision tree models within an ensemble. The main findings of the investigations are presented and discussed in the paper.

Item Type: Conference or Workshop Item (Paper)
Faculty \ School: Faculty of Science > School of Computing Sciences
University of East Anglia > Faculty of Science > Research Groups > Computational Biology (subgroups are shown below) > Machine learning in computational biology
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Depositing User: Vishal Gautam
Date Deposited: 14 Jun 2011 11:46
Last Modified: 25 Jul 2018 01:45
URI: https://ueaeprints.uea.ac.uk/id/eprint/22402
DOI: 10.1109/IJCNN.2006.247244

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