Several papers have already shown the interest of using multiple classifiers in order to enhance the performance of biometric person authentication systems. In this paper, we would like to argue that the core task of Biometric Person Authentication is ...

In this paper, we present some recent developments of Multiple Classifiers Systems (MCS) for remote sensing applications. Some standard MCS methods (boosting, bagging, consensus theory and random forests) are briefly described and applied to multisource ...

We describe an ensemble of classifiers based algorithm for incremental learning in nonstationary environments. In this formulation, we assume that the learner is presented with a series of training datasets, each of which is drawn from a different ...

A new optimization technique is proposed for classifier fusion -- Cooperative Coevolutionary Ensemble Learning (CCEL). It is based on a specific multipopulational evolutionary algorithm -- cooperative coevolution. It can be used as a wrapper over any ...

Rotation Forest is a recently proposed method for building classifier ensembles using independently trained decision trees. It was found to be more accurate than bagging, AdaBoost and Random Forest ensembles across a collection of benchmark data sets. ...

Ensemble methods with Random Oracles have been proposed recently (Kuncheva and Rodríguez, 2007). A random-oracle classifier consists of a pair of classifiers and a fixed, randomly created oracle that selects between them. Ensembles of random-oracle ...

We investigate the theoretical links between a regression ensemble and a linearly combined classification ensemble. First, we reformulate the Tumer & Ghosh model for linear combiners in a regression context; we then exploit this new formulation to ...

An ensemble of classifiers (EoC) has been shown to be effective in improving classifier performance. To optimize EoC, the ensemble selection is one of the most imporatant issues. Dynamic scheme urges the use of different ensembles for different samples, ...

In this paper we present a new method for fusing classifiers output for problems with a number of classes M > 2. We extend the well-known Behavior Knowledge Space method with a hierarchical approach of the different cells. We propose to add the ranking ...

Combining multiple classifier systems (MCS') has been shown to outperform single classifier system. It has been demonstrated that improvement for ensemble performance depends on either the diversity among or the performance of individual systems. A ...

Ensembles are often capable of greater predictive performance than any of their individual classifiers. Despite the need for classifiers to make different kinds of errors, the majority voting scheme, typically used, treats each classifier as though it ...

Matching systems can be used in different operation tasks such as verification task and identification task. Different optimization criteria exist for these tasks - reducing cost of acceptance decisions for verification systems and minimizing ...

We present three new voting schemes for multiclassifier biometric authentication using a reliability model to influence the importance of each base classifier's vote. The reliability model is a meta-classifier computing the probability of a correct ...

The use of quality measures in pattern classification has recently received a lot of attention in the areas where the deterioration of signal quality is one of the primary causes of classification errors. An example of such domain is biometric ...

Face as a biometric is known to be sensitive to different factors, e.g., illumination condition and pose. The resultant degradation in face image quality affects the system performance. To counteract this problem, we investigate the merit of combining a ...

Error Correcting Output Coding (ECOC) is an established technique to face a classification problem with many possible classes decomposing it into a set of two class subproblems. In this paper, we propose an ECOC system with a reject option that is ...

Classifier combining rules are designed for the fusion of the results from the component classifiers in a multiple classifier system. In this paper, we firstly propose a theoretical explanation of one important classifier combining rule, the sum rule, ...

Because of the lack of a clear guideline or technique for selecting classifiers which maximise diversity and accuracy, the development of techniques for analysing classifier relationships and methods for generating good constituent classifiers remains ...

We propose a new classifier combination scheme for the ensemble of classifiers. The Pairwise Fusion Matrix (PFM) constructs confusion matrices based on classifier pairs and thus offers the estimated probability of each class based on each classifier ...

A new theoretical framework for the analysis of linear combiners is presented in this paper. This framework extends the scope of previous analytical models, and provides some new theoretical results which improve the understanding of linear combiners ...