Atypical observations, which are called outliers, are one of difficulties to apply standard Gaussian density based pattern classification methods. Large number of outliers makes di...
The ECOC framework provides a powerful and popular method for solving multiclass problems using a multitude of binary classifiers. We had recently introduced the Binary Hierarchica...
An ensemble of classifiers based algorithm, Learn++, was recently introduced that is capable of incrementally learning new information from datasets that consecutively become avail...
Michael Muhlbaier, Apostolos Topalis, Robi Polikar
Abstract. One of the potential advantages of multiple classifier systems is an increased robustness to noise and other imperfections in data. Previous experiments on classificati...
Despite the good results provided by Dynamic Classifier Selection (DCS) mechanisms based on local accuracy in a large number of applications, the performances are still capable of ...
This paper describes a method for fusing a collection of classifiers where the fusion can compensate for some positive correlation among the classifiers. Specifically, it does not ...
We experimentally evaluate bagging and six other randomization-based approaches to creating an ensemble of decision-tree classifiers. Bagging uses randomization to create multipl...
Robert E. Banfield, Lawrence O. Hall, Kevin W. Bow...
Abstract. This paper presents a probabilistic model for combining cluster ensembles utilizing information theoretic measures. Starting from a co-association matrix which summarizes...
Recent works about perceptron-based fusion of multiple fingerprint matchers showed the effectiveness of such approach in improving the performance of personal identity verification...