A comparison of random forest with ECOC-based classifiers

Authors:
R. S. Smith;M. Bober;T. Windeatt
Affiliations:
Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, Surrey, UK;Mitsubishi Electric R&D Centre Europe B.V, Guildford, Surrey, UK;Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, Surrey, UK
Venue:
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Year:
2011

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Abstract

We compare experimentally the performance of three approaches to ensemble-based classification on general multi-class datasets. These are the methods of random forest, error-correcting output codes (ECOC) and ECOC enhanced by the use of bootstrapping and classseparability weighting (ECOC-BW). These experiments suggest that ECOC-BW yields better generalisation performance than either random forest or unmodified ECOC. A bias-variance analysis indicates that ECOC benefits from reduced bias, when compared to random forest, and that ECOC-BW benefits additionally from reduced variance. One disadvantage of ECOC-based algorithms, however, when compared with random forest, is that they impose a greater computational demand leading to longer training times.