Individual classification models are recently challenged by the combined pattern recognition systems, which often show better performance. In such systems the optimal set of classifiers is first selected and then combined by a specific combination method. Large and rough search space formed from performances of various combinations of classifiers makes the selection process very difficult and o...

The paper presents new results concerning selection of optimal information fusion formula for ensembles of monitoring system channels. The goal of information fusion is to create an integral classificator designed for effective classification of targeted events, which appear in the vicinity of monitored object. The LPBoost (LP-β and LP-B variants), the Multiple Kernel Learning, and Weighing of ...

We consider the trainable fusion rule design problem when the expert classifiers provide crisp outputs and the behavior space knowledge method is used to fuse local experts’ decisions. If the training set is utilized to design both the experts and the fusion rule, the experts’ outputs become too self-assured. In small sample situations, “optimistically biased” experts’ outputs bluffs the fusion...

An effective fusion scheme is necessary for combining features from multiple biometric traits. This paper presents a method of fusion using multiple features from hand vein biometric traits for Multimodal biometric recognition. In the proposed method, a biometric authentication system using three different set of veins images, such as, finger vein, palm vein and dorsal vein is developed. Here t...

This chapter presents the design and development of WebDisC, a knowledge-based Web information system for the fusion of classifiers induced at geographically distributed databases. The main features of our system are: i) a declarative rule language for classifier selection that allows the combination of syntactically heterogeneous distributed classifiers, ii) a variety of standard methods for f...

A novel fusion method for semantic concept detection in images, called tree fusion, is proposed. Various kinds of features are given to different classifiers. Then, according to the importance of features and effectiveness of classifiers, the results of feature-classifier pairs are ranked and fused using C4.5 algorithm. Experimental results conducted on the MSRC and PASCAL VOC 2007 datasets hav...

This paper emphasizes the benefits of embedding data categorization within fusion of classifiers for text-independent speaker verification. A selective fusion framework is presented which considers data idiosyncrasies by assigning particular test samples to appropriate fusion schemes. As an extension, incompatible data can be spotted and excluded from inherent classification errors. In addition...

Individual classification models are recently challenged by combined pattern recognition systems, which often show better performance. In such systems the optimal set of classifiers is first selected and then combined by a specific fusion method. For a small number of classifiers optimal ensembles can be found exhaustively, but the burden of exponential complexity of such search limits its prac...

In the past decade, several rules for fusion of pattern classifiers’ outputs have been proposed. Although imbalanced classifiers, that is, classifiers exhibiting very different accuracy, are used in many practical applications (e.g., multimodal biometrics for personal identity verification), the conditions of classifiers’ imbalance under which a given rule can significantly outperform another o...