Bauer, E. and Kohavi, R. (1999). An empirical comparison of voting classification algorithms: Bagging, boosting and variants. Machine Learning, 36(1-2):105--142. Home/Search Document Details and Download
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....when given 40 or more of the data. Note that even with large amounts of training data, DECORATE s performance is quite competitive with Adaboost given 100 of the data DECORATE produces higher accuracies on 6 out of 15 data sets. It has been observed in this and previous studies [Webb, 2000; Bauer and Kohavi, 1999] that while AdaBoost usually significantly reduces the error of the base learner, it occasionally increases it, often to a large extent. DECORATE does not have this problem as is clear from Table 2. There are only two cases where DECORATE significantly lowers accuracy compared to J48, and both ....
....2000] Our current study has used J48 as a base learner; however, we would expect similarly good results with other base learners. Decision tree induction has been the most commonly used base learner in other ensemble studies, but there has been some work using neural networks and naive Bayes [Bauer and Kohavi, 1999; Opitz and Maclin, 1999] Experiments on DECORATing other learners is another area for future work. 7 Conclusion By manipulating artificial training examples, DECORATE is able to use a strong base learner to produce an effective, diverse ensemble. Experimental results demonstrate that the ....
E. Bauer and R. Kohavi. An empirical comparison of voting classification algorithms: Bagging, boosting and variants. Machine Learning, 36, 1999.
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.... 80996 69346 90815 69984 94400 70325 96153 3000 43312 41212 42917 59293 44977 67222 45130 70802 46139 71660 Table 6: Benefits (US ) using Single Classifiers and Classifier Ensembles (original stream) sembles include changing the instances used for training through techniques such as Bagging [3] and Boosting [12] The classifier ensembles have several advantages over single model classifiers. First, classifier ensembles offer a significant improvement in prediction accuracy [12, 24] Second, building a classifier ensemble is more efficient than building a single model, since most model ....
Eric Bauer and Ron Kohavi. An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36(1-2):105--139, 1999.
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Bauer, E. and Kohavi, R. (1999). An empirical comparison of voting classification algorithms: Bagging, boosting and variants. Machine Learning, 36(1-2):105--142.
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Bauer, E., & Kohavi, R. (1999). An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36, 105-- 139.
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Bauer, E. and Kohavi, R. (1999). An empirical comparison of voting classification algorithms: bagging, boosting, and variants. Machine Learning 36, 105--139.
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Bauer E, Kohavi R. (1999) An empirical comparison of voting classification algorithms: Bagging, Boosting and variants. Machine Learning 36, 105-139
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Bauer E, Kohavi R. (1999) An empirical comparison of voting classification algorithms: bagging, boosting and variants. Machine learning 36, 105-142
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Bauer, E., & Kohavi, R. (1999). An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting and Variants. Machine Learning, 36, 105--139.
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Bauer, E., Kohavi, R.: An empirical comparison of voting classification algorithms: bagging, boosting, and variants. machine learning 36 (1999) 105--139
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