Ensemble methods are considered the state‐of‐the art solution for many machine learning
challenges. Such methods improve the predictive performance of a single model by training …

Ensemble-based methods are among the most widely used techniques for data stream
classification. Their popularity is attributable to their good performance in comparison to …

A decision tree is a predictive model that recursively partitions the covariate's space into
subspaces such that each subspace constitutes a basis for a different prediction function …

The idea of ensemble methodology is to build a predictive model by integrating multiple
models. It is well-known that ensemble methods can be used for improving prediction …

Decision trees have become one of the most powerful and popular approaches in
knowledge discovery and data mining; it is the science of exploring large and complex …

Breiman's bagging and Freund and Schapire's boosting are recent methods for improving
the predictive power of classifier learning systems. Both form a set of classifiers that are …

Stacked generalization is a general method of using a high-level model to combine lower-
level models to achieve greater predictive accuracy. In this paper we address two crucial …

This paper proposes an innovative fraud detection method, built upon existing fraud
detection research and Minority Report, to deal with the data mining problem of skewed data …

Very large databases with skewed class distributions and non-unlform cost per error are not
uncommon in real-world data mining tasks. We devised a multi-classifier meta-learning …

1. Introduction to pattern classification. 1.1. Pattern classification. 1.2. Induction algorithms.
1.3. Rule induction. 1.4. Decision trees. 1.5. Bayesian methods. 1.6. Other induction …