Classifier ensembles constitute one of the main research directions in machine learning and
data mining. The use of multiple classifiers generally allows better predictive performance …

This paper, with an aim at improving neural networks' generalization performance, proposes
an effective neural network ensemble approach with two novel ideas. One is to apply neural …

Ensemble pruning is crucial for the consideration of both efficiency and predictive accuracy
of an ensemble system. This paper proposes a new Competitive technique for Ensemble …

A novel evolutionary approach is proposed for effective intrusion detection based on
benchmark datasets. The proposed approach can generate a pool of noninferior individual …

Nowadays, in pattern recognition and classification, does not exist a dominant classifier for
all data distributions. Also, data distribution of the task at hand is usually unknown, ie there is …

Overproduce-and-select (OPAS) is a frequently used paradigm for building ensembles. In
static OPAS, a large number of base classifiers are trained, before a subset of the available …

Classification is application areas of neural networks. To generate an algorithm to classify
multiclass and single class datasets to achieve high diversity and more accuracy. Ensemble …

In this paper, an ensemble classifier, namely RotaSVM, is proposed that uses recently
developed rotational feature selection approach and Support Vector Machine classifier …

The most challenging issue in constructing a classification system based on ensemble is
how to construct an appropriate ensemble of basic classifiers. In this paper, a new approach …

An Efficient Multiclass Classification Method Based on Classifier Selection Technique Page 1
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