This paper proposes a boosting-like method to train a classifier ensemble from data streams. It naturally adapts to concept drift and allows to quantify the drift in terms of its base learners. The algorithm is empirically shown to outperform learning algorithms that ignore concept drift. It performs no worse than advanced adaptive time window and example selection strategies that store all the...

Ensemble rule based classification methods have been popular for a while in the machine-learning literature (Hand, 1997). Given the advent of low-cost, high-computing power, we are curious to see how far can we go by repeating some basic learning process, obtaining a variety of possible inferences, and finally basing the global classification decision on some sort of ensemble summary. Some gene...

Ensemble rule based classification methods have been popular for a while in the machine-learning literature (Hand, 1997). Given the advent of low-cost, high-computing power, we are curious to see how far can we go by repeating some basic learning process, obtaining a variety of possible inferences, and finally basing the global classification decision on some sort of ensemble summary. Some gene...

Recent advances in the study of voting classification algorithms have brought empirical and theoretical results clearly showing the discrimination power of ensemble classifiers. It has been previously argued that the search of this classification power in the design of the algorithms has marginalized the need to obtain interpretable classifiers. Therefore, the question of whether one might have...

We propose a novel approach for using unsupervised boosting to create an ensemble of generative models, where models are trained in sequence to correct earlier mistakes. Our metaalgorithmic framework can leverage any existing base learner that permits likelihood evaluation, including recent deep expressive models. Further, our approach allows the ensemble to include discriminative models traine...

In this paper we propose a novel approach for ensemble construction based on the use of linear projections to achieve both accuracy and diversity of individual classifiers. The proposed approach uses the philosophy of boosting, putting more effort on difficult instances, but instead of learning the classifier on a biased distribution of the training set it uses misclassified instances to find a...

Learning similarity functions between image pairs with deep neural networks yields highly correlated activations of large embeddings. In this work, we show how to improve the robustness of embeddings by exploiting independence in ensembles. We divide the last embedding layer of a deep network into an embedding ensemble and formulate training this ensemble as an online gradient boosting problem....

If we look a few years back, we will find that ensemble classification model has outbreak many research and publication in the data mining community discussing how to combine models or model prediction with reduction in the error that results. When we ensemble the prediction of more than one classifier, more accurate and robust models are generated. We have convention that bagging, boosting wit...

Ensemble models combine two or more models to enable a more robust prediction, classification, or variable selection. This paper describes three types of ensemble models: boosting, bagging, and model averaging. It discusses go-to methods, such as gradient boosting and random forest, and newer methods, such as rotational forest and fuzzy clustering. The examples section presents a quick setup th...

The combination of classifiers has long been proposed as a method to improve the accuracy achieved in isolation by a single classifier. In contrast to such wellexplored methods as boosting and bagging, we are interested in ensemble methods that allow the combination of heterogeneous sets of classifiers, which are classifiers built using differing learning paradigms. We focus on theoretical and ...