Recently, classifier ensemble methods are gaining more and more attention in the machine-learning and data-mining communities. In most cases, the performance of an ensemble is better than a single classifier. Many methods for creating diverse classifiers were developed during the past decade. When these diverse classifiers are generated, it is important to select the proper base classifier to j...

Decreasing the individual error and increasing the diversity among classifiers are two crucial factors for improving ensemble performances. Nevertheless, the ‘‘kappa-error’’ diagram shows that enhancing the diversity is at the expense of reducing individual accuracy. Hence, a newmethod namedMatching Pursuit Optimization Ensemble Classifiers (MPOEC) is proposed in this paper in order to balance ...

We introduce a novel ensemble model based on random projections. The contribution of using random projections is two-fold. First, the randomness provides the diversity which is required for the construction of an ensemble model. Second, random projections embed the original set into a space of lower dimension while preserving the dataset’s geometrical structure to a given distortion. This reduc...

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 for homogeneous ensemble classifiers using bagging and heterogeneous ensemble classifiers using arcing classifier and their performa...

This paper presents an ensemble of neo-cognitron neural network base classifiers to enhance the accuracy of the system, along the experimental results. The method offers lesser computational preprocessing in comparison to other ensemble techniques as it ex-preempts feature extraction process before feeding the data into base classifiers. This is achieved by the basic nature of neo-cognitron, it...

Classifier ensemble generally should combine diverse component classifiers. However, it is difficult to give a definitive connection between diversity measure and ensemble accuracy. Given a list of available component classifiers, how to adaptively and diversely ensemble classifiers becomes a big challenge in the literature. In this paper, we argue that diversity, not direct diversity on sample...

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 ...

Ensemble methods take their output from a set of base predictors. The ensemble accuracy depends on two factors: the base classifiers accuracy and their diversity (how different are these base classifiers outputs from each other). An approach for increasing the diversity of the base classifiers is presented in this paper. The method builds some new features to be added to the base classifier tra...

In this study, a novel weighted ensemble classifier that improves classification accuracy and minimizes the number of classifiers is proposed. Proposed method uses sparsity techniques therefore it is named sparsity-driven weighted ensemble classifier (SDWEC). In SDWEC, ensemble weight finding problem is modeled as a cost function with following terms: (a) a data fidelity term aiming to decrease...

Previous work done in genre recognition and characterization from symbolic sources (monophonic melodies extracted from MIDI files) have pointed our research to the use of classifier ensembles to better accomplish the task. This work presents current research in the use of voting ensembles of classifiers trained on statistical description models of melodies, in order to improve both the accuracy...