When more than a single classifier has been trained for the same recognition problem the
question arises how this set of classifiers may be combined into a final decision rule. Several …

The extraction of discriminant features is the most fundamental and important problem in
face recognition. This paper presents a method to extract optimal discriminant features for …

This paper presents a combination of classifier selection and fusion by using statistical
inference to switch between the two. Selection is applied in those regions of the feature …

Nowadays, the number of protein sequences being stored in central protein databases from
labs all over the world is constantly increasing. From these proteins only a fraction has been …

Support vector machines (SVM) is originally designed for binary classification. To extend it to
multi-class scenario, a typical conventional way is to decompose an M-class problem into a …

Using a number of measures for characterising the complexity of classification problems, we
studied the comparative advantages of two methods for constructing decision forests …

The concept of a classifier competence is fundamental to multiple classifier systems (MCSs).
In this study, a method for calculating the classifier competence is developed using a …

In the real world concepts are often not stable but change with time. A typical example of this
in the biomedical context is antibiotic resistance, where pathogen sensitivity may change …

Multiple classi" er systems (MCSs) based on the combination of a set of di! erent classi" ers
are currently used to achieve high pattern-recognition performances [1]. For each pattern …

Classifier combination methods have proved to be an effective tool to increase the
performance of pattern recognition applications. In this chapter we review and categorize …