Learning from imbalanced data is an important problem in data mining research. Much research has addressed the problem of imbalanced data by using sampling methods to generate an equally balanced training set to improve the performance of the prediction models, but it is unclear what ratio of class distribution is best for training a prediction model. Bagging is one of the most popular and effe...

Two bagging approaches, say 1 2 n-out-of-n without replacement (subagging) and n-out-of-n with replacement (bagging) have been applied in the problem of estimation of the parameters in a multivariate mixture model. It has been observed by Monte Carlo simulations and a real data example, that both bagging methods have improved the standard deviation of the maximum likelihood estimator of the mix...

Bagging is a method of obtaining more robust predictions when the model class under consideration is unstable with respect to the data, i.e., small changes in the data can cause the predicted values to change significantly. In this paper, we introduce a Bayesian version of bagging based on the Bayesian bootstrap. The Bayesian bootstrap resolves a theoretical problem with ordinary bagging and of...

Underlying fabrics can change the appearance, function and quality of the garment, and also add so much longevity of the garment. Nowadays, with the increasing use of various types of fabrics in the garment industry, their resistance to bagging is of great importance with the aim of determining the effectiveness of textiles under various forces. The current paper investigated the effect of unde...

Ensemble classification combines individually trained classifiers to obtain more accurate predictions than individual classifiers alone. Ensemble techniques are very useful for improving the generalizability of the classifier. Bagging is the method used most commonly for constructing ensemble classifiers. In bagging, different training data subsets are drawn randomly with replacement from the o...

The error rate of decision-tree and other classi-cation learners can often be much reduced by bagging: learning multiple models from bootstrap samples of the database, and combining them by uniform voting. In this paper we empirically test two alternative explanations for this, both based on Bayesian learning theory: (1) bagging works because it is an approximation to the optimal procedure of B...

This paper presents the application of the bagging technique for non-linear regression models to obtain more accurate and robust calibration of spectroscopy. Bagging refers to the combination of multiple models obtained by bootstrap re-sampling with replacement into an ensemble model to reduce prediction errors. It is well suited to “non-robust” models, such as the non-linear calibration method...

Decision tree (DT) is one of the most popular classification algorithms in data mining and machine learning. However, the performance of DT based credit scoring model is often relatively poorer than other techniques. This is mainly due to two reasons: DT is easily affected by (1) the noise data and (2) the redundant attributes of data under the circumstance of credit scoring. In this study, we ...

Underlying fabrics can change the appearance, function and quality of the garment, and also add so much longevity of the garment. Nowadays, with the increasing use of various types of fabrics in the garment industry, their resistance to bagging is of great importance with the aim of determining the effectiveness of textiles under various forces. The current paper investigated the effect of unde...

Bagging predictors is a method for generating multiple versions of a predictor and using these to get an aggregated predictor. The aggregation averages over the versions when predicting a numerical outcome and does a plurality vote when predicting a class. The multiple versions are formed by making bootstrap replicates of the learning set and using these as new learning sets. Tests on real and ...