In this paper we make an extensive study of different methods for building ensembles of classifiers. We examine variants of ensemble methods that are based on perturbing features. We illustrate the power of using these variants by applying them to a number of different problems. We find that the best performing ensemble is obtained by combining an approach based on random subspace with a cluste...

The scenario of classification with imbalanced datasets has gained a notorious significance in the last years. This is due to the fact that a large number of problems where classes are highly skewed may be found, affecting the global performance of the system. A great number of approaches have been developed to address this problem. These techniques have been traditionally proposed under three ...

Despite the recent advances in Molecular Biology, the function of a large amount of proteins is still unknown. An approach that can be used in the prediction of a protein function consists of searching against secondary databases, also known as signature databases. Different strategies can be applied to use protein signatures in the prediction of function of proteins. A sophisticated approach c...

In many datasets, the samples are related by a known image transformation, such as rotation, or a repeatable non-rigid deformation. This applies to both datasets with the same objects under different viewpoints, and datasets augmented with virtual samples. Such datasets possess a high degree of redundancy, because geometrically-induced transformations should preserve intrinsic properties of the...

The article suggests an algorithm for regular classifier ensemble methodology. The proposed methodology is based on possibilistic aggregation to classify samples. The argued method optimizes an objective function that combines environment recognition, multi-criteria aggregation term and a learning term. The optimization aims at learning backgrounds as solid clusters in subspaces of the high...

Broad classes of statistical classification algorithms have been developed and applied successfully to a wide range of real world domains. In general, ensuring that the particular classification algorithm matches the properties of the data is crucial in providing results that meet the needs of the particular application domain. One way in which the impact of this algorithm/application match can...

classification ensemble, which uses the weighed polling of outputs, is the art of combining a set of basic classifiers for generating high-performance, robust and more stable results. this study aims to improve the results of identifying the persian handwritten letters using error correcting output coding (ecoc) ensemble method. furthermore, the feature selection is used to reduce the costs of ...

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.

The relationship between ensemble classifier performance and the diversity of the predictions made by ensemble base classifiers is explored in the context of heterogeneous ensemble classifiers. Specifically, numerical studies indicate that heterogeneous ensembles can be generated from base classifiers of homogeneous ensemble classifiers that are both significantly more accurate and diverse than...

An ideal ensemble is composed of base classifiers that perform well and that have minimal overlap in their errors. Eliminating classifiers from an ensemble based on a criterion that reflects poor classification performance and error redundancy with peer classifiers can improve ensemble performance. The Diversity Networks method asymmetrically evaluates each pair of classifiers as a linear combi...