Combining a set of classifiers has often been exploited to improve the classification performance. Accurate as well as diverse base classifiers are prerequisite to construct a good ensemble classifier. Therefore, estimating diversity among classifiers has been widely investigated. This paper presents an ensemble approach that combines a set of diverse rules obtained by genetic programming. Gene...

The Ensemble of Classifiers (EoC) has been shown to be effective in improving the performance of single classifiers by combining their outputs, and one of the most important properties involved in the selection of the best EoC from a pool of classifiers is considered to be classifier diversity. In general, classifier diversity does not occur randomly, but is generated systematically by various ...

One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. In this research work, new ensemble classification methods are proposed with homogeneous ensemble classifier using bagging and heterogeneous ensemble classifier using arcing and their performances are ana...

This paper presents an approach with ensemble classifiers using unsupervised data selection for speaker recognition. Ensemble learning is a type of machine learning that applies a combination of several weak learners to achieve an improved performance than a single learner. Based on its acoustic characteristics, the speech utterance is divided into several subsets using unsupervised data select...

In this paper, first, an initial feature vector for vocal fold pathology diagnosis is proposed. Then, for optimizing the initial feature vector, a genetic algorithm is proposed. Some experiments are carried out for evaluating and comparing the classification accuracies which are obtained by the use of the different classifiers (ensemble of decision tree, discriminant analysis and K-nearest neig...

From the advent of the application of satellite imagery to land cover mapping, one of the growing areas of research interest has been in the area of image classification. Image classifiers are algorithms used to extract land cover information from satellite imagery. Most of the initial research has focussed on the development and application of algorithms to better existing and emerging classif...

Neural network ensemble techniques have been shown to be very accurate classification techniques. However, in some real-life applications a number of classifiers required to achieve a reasonable accuracy is enormously large and hence very space consuming. This paper proposes several methods for pruning neural network ensembles. The clustering based approach applies k-means clustering to entire ...

The success of an ensemble of classifiers depends on the diversity of the underlying features. If a classifier can address more different aspects of the analyzed objects, this allows to improve an ensemble. In this paper we propose an ensemble using as classifier members a Hopfield Neural Network (HNN) that uses Haar-like features as an input template. We analyse the HNN as the only classifier ...

Generalized additive models (GAMs) are a generalization of generalized linear models (GLMs) and constitute a powerful technique which has successfully proven its ability to capture nonlinear relationships between explanatory variables and a response variable in many domains. In this paper, GAMs are proposed as base classifiers for ensemble learning. Three alternative ensemble strategies for bin...

This paper proposes a new ensemble method that constructs an ensemble of tree-structured classifiers using multi-view learning. We are motivated by the fact that an ensemble can outperform its members providing that these classifiers are diverse and accurate. In order to construct diverse individual classifiers, we assume that the object to be classified is described by multiple feature sets (v...