Trimmed Bagging

Croux, Christophe; Joossens, Kristel; Lemmens, Aurélie

doi:10.2139/ssrn.1089358

Cited by 7 publications

(12 citation statements)

References 10 publications

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“…They carried out experiments on benchmark datasets and recommend from the results that robust bagging performs quite similar compare with the standard bagging when applied to unstable base classifiers such as decision trees, but performs better when applied to more stable base classifiers as Fisher Linear Discriminant Analysis and Nearest Mean Classifier. Similarly, in Croux et al (2007) experiments, trimmed bagging performs comparably to standard bagging when applied to decision trees, but yields better results when applied to more stable base classifiers, like support vector machines. presented a study of different selective bagging techniques using decision stump as base classifier.…”

Section: Selective Bagging Variantsmentioning

confidence: 91%

Bagging and boosting variants for handling classifications problems: a survey

Kotsiantis

2013

The Knowledge Engineering Review

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Bagging and boosting are two of the most well-known ensemble learning methods due to their theoretical performance guarantees and strong experimental results. Since bagging and boosting are an effective and open framework, several researchers have proposed their variants, some of which have turned out to have lower classification error than the original versions. This paper tried to summarize these variants and categorize them into groups. We hope that the references cited cover the major theoretical issues, and provide access to the main branches of the literature dealing with such methods, guiding the researcher in interesting research directions.

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Section: Selective Bagging Variantsmentioning

confidence: 91%

Bagging and boosting variants for handling classifications problems: a survey

Kotsiantis

2013

The Knowledge Engineering Review

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“…The AP procedure adopted similar reduction rule as nice bagging (Nice) [25] and trimmed bagging (TB) [26] in which only good or "nice" bootstrap versions of the base models validated on out-ofbag samples were aggregated. Specially, those base models generated in the traditional bagging, which performed better than the rest ones according to certain decile value , were retained to comprise the final reduced set of base models.…”

Section: Accuracy-based Pruning Methods (Ap)mentioning

confidence: 99%

“…These two methods can be performed by a combination way in any order that finally comprised the two-stage strategy. The former, i.e., the AP procedure, used similar rule as the nice bagging [25] and the trimmed bagging [26] by excluding the worst classifiers and aggregated the rest. Specially, for all models established in the traditional bagging, those base models that had the highest prediction performance measured using accuracy (or the lowest error rates) validated on their out-of-bag samples were selected and retained.…”

Section: Introductionmentioning

confidence: 99%

Two‐Stage Bagging Pruning for Reducing the Ensemble Size and Improving the Classification Performance

Zhang

Song

Jiang

et al. 2019

Mathematical Problems in Engineering

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Ensemble methods, such as the traditional bagging algorithm, can usually improve the performance of a single classifier. However, they usually require large storage space as well as relatively time-consuming predictions. Many approaches were developed to reduce the ensemble size and improve the classification performance by pruning the traditional bagging algorithms. In this article, we proposed a two-stage strategy to prune the traditional bagging algorithm by combining two simple approaches: accuracy-based pruning (AP) and distance-based pruning (DP). These two methods, as well as their two combinations, “AP+DP” and “DP+AP” as the two-stage pruning strategy, were all examined. Comparing with the single pruning methods, we found that the two-stage pruning methods can furthermore reduce the ensemble size and improve the classification. “AP+DP” method generally performs better than the “DP+AP” method when using four base classifiers: decision tree, Gaussian naive Bayes, K-nearest neighbor, and logistic regression. Moreover, as compared to the traditional bagging, the two-stage method “AP+DP” improved the classification accuracy by 0.88%, 4.06%, 1.26%, and 0.96%, respectively, averaged over 28 datasets under the four base classifiers. It was also observed that “AP+DP” outperformed other three existing algorithms Brag, Nice, and TB assessed on 8 common datasets. In summary, the proposed two-stage pruning methods are simple and promising approaches, which can both reduce the ensemble size and improve the classification accuracy.

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“…Croux et al [37] propose the idea of trimmed bagging which aims to prune classifiers that yield the highest error rates, as estimated by the out-of-bag error rate. It has been shown that trimmed bagging performs comparably to standard bagging when applied to unstable classifiers as decision trees, but yields improved accuracy when applied to more stable base classifiers, like support vector machines.…”

Section: Post-combining Pruningmentioning

confidence: 99%