Smart embedded systems often run sophisticated pattern recognition algorithms and are
found in many areas like automotive, sports and medicine. The developer of such a system …

Embedded microcontrollers are employed in an increasing number of applications as a
target for the implementation of classification systems. This is true for example for the fields …

In many classification systems, sensing modalities have different acquisition costs. It is often
unnecessary to use every modality to classify a majority of examples. We study a multi-stage …

Although usually classifier error is the main concern in publications, in real applications
classifier evaluation complexity may play a large role as well. In the paper, a simple …

This study explores the scope for achieving enhanced recognition system performance
through deployment of a composite classifier system consisting of two or more component …

Classification is an important field of data mining problems. Given a set of labeled training
examples the classification task constructs a classifier. A classifier is a global model which is …

Over the recent years, research on Learning Classifier Systems (LCSs) got more and more
pronounced and diverse. There have been significant advances of the LCS field on various …

Bagging and boosting are popular and often successful ways to improve the performance of
a classifier by means of aggregation. Classifiers can also be aggregated by means of an …

The classification rate is the most significant factor affecting the selection of an appropriate
classification approach for achieving the desired quality of decisions. Several researchers …

The evaluation of classifier performance in a cost-sensitive setting is straightforward if the
operating conditions (misclassification costs and class distributions) are fixed and known …