A Comparison of Random Forest with ECOC-Based Classifiers
نویسندگان
چکیده
We compare experimentally the performance of three approaches to ensemble-based classi cation 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 ECOCBW yields better generalisation performance than either random forest or unmodi ed ECOC. A bias-variance analysis indicates that ECOC bene ts from reduced bias, when compared to random forest, and that ECOC-BW bene ts 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.
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