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A comparison of random forest with ECOC-based classifiers

Smith, RS, Bober, M and Windeatt, T (2011) A comparison of random forest with ECOC-based classifiers Lecture Notes in Computer Science: Multiple Classifier Systems, 6713. pp. 207-216.

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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 class-separability 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.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
Smith, RS
Bober, M
Windeatt, T
Date : 2011
DOI : 10.1007/978-3-642-21557-5_23
Contributors :
Additional Information : The original publication is available at
Depositing User : Symplectic Elements
Date Deposited : 22 Mar 2012 10:41
Last Modified : 31 Oct 2017 14:16

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