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...

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 ...

Bagging frequently improves the predictive performance of a model. An online version has recently been introduced, which attempts to gain the benefits of an online algorithm while approximating regular bagging. However, regular online bagging is an approximation to its batch counterpart and so is not lossless with respect to the bagging operation. By operating under the Bayesian paradigm, we in...

We propose two new improvements for bagging methods on evolving data streams. Recently, two new variants of Bagging were proposed: ADWIN Bagging and Adaptive-Size Hoeffding Tree (ASHT) Bagging. ASHT Bagging uses trees of different sizes, and ADWIN Bagging uses ADWIN as a change detector to decide when to discard underperforming ensemble members. We improve ADWIN Bagging using Hoeffding Adaptive...

The development of the smart grid has resulted in new requirements for fault prediction of power transformers. This paper presents an entropy-based Bagging (E-Bagging) method for prediction of characteristic parameters related to power transformers faults. A parameter of comprehensive information entropy of sample data is brought forward to improve the resampling process of the E-Bagging method...

Various modifications of bagging for class imbalanced data are discussed. An experimental comparison of known bagging modifications shows that integrating with undersampling is more powerful than oversampling. We introduce Local-and-Over-All Balanced bagging where probability of sampling an example is tuned according to the class distribution inside its neighbourhood. Experiments indicate that ...