Ensemble methodology, which builds a classification model by integrating multiple
classifiers, can be used for improving prediction performance. Researchers from various …

Classification of data with imbalanced class distribution has posed a significant drawback of
the performance attainable by most standard classifier learning algorithms, which assume a …

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 …

Artificial intelligence (AI) is a scientific discipline that aims to create intelligent machines.
Machine learning is a popular and practical AI subfield that aims to automatically improve …

This paper presents a novel cluster-oriented ensemble classifier. The proposed ensemble
classifier is based on original concepts such as learning of cluster boundaries by the base …

This paper introduces a novel concept for creating an ensemble of classifiers. The concept is
based on generating an ensemble of classifiers through clustering of data at multiple layers …

Here, we consider a two-level (four classes in level 1 and 27 folds in level 2) protein fold
determination problem. We propose several new features and use some existing features …

Recognition of medical concepts is a basic step in information extraction from clinical
records. We wished to improve on the performance of a variety of concept recognition …

This paper presents cluster‐based ensemble classifier–an approach toward generating
ensemble of classifiers using multiple clusters within classified data. Clustering is …

This paper proposes a hybrid genetic algorithm for classifier ensemble selection. In this
paper, two local search operations used to improve offspring prior to replacement are …