Many approaches to active learning involve periodically training one classifier and choosing data points with the lowest confidence. An alternative approach is to periodically choose data instances that maximize disagreement among the label predictions across an ensemble of classifiers. Many classifiers with different underlying structures could fit this framework, but some ensembles are more s...

Ensemble learning can be used to increase the overall classification accuracy of a classifier by generating multiple base classifiers and combining their classification results. A frequently used family of base classifiers for ensemble learning are decision trees. However, alternative approaches can potentially be used, such as the Prism family of algorithms which also induces classification ru...

Vibration-based quality monitoring of manufactured components often employs pattern recognition methods. Albeit developing several classification methods, they usually provide high accuracy for specific types datasets, but not general cases. In this paper, issue has been addressed by a novel ensemble classifier based on the Dempster-Shafer theory evidence. proposed procedure, prior to DST combi...

Ensemble classifier refers to a group of individual classifiers that are cooperatively trained on data set in a supervised classification problem. In this paper we present a review of commonly used ensemble classifiers in the literature. Some ensemble classifiers are also developed targeting specific applications. We also present some application driven ensemble classifiers in this paper.