A survey of multiple classifier systems as hybrid systems

A survey of multiple classifier systems as hybrid systems

A current focus of intense research in pattern classification is the combination of several classifier systems, which can be built following either the same or different models and/or datasets building approaches. These systems perform information fusion of classification decisions at different levels overcoming limitations of traditional approaches based on single classifiers. This paper prese...

Learning classifier systems: a survey

Learning Classifier Systems (LCSs) are rule-based systems that automatically build their ruleset. At the origin of Holland’s work, LCSs were seen as a model of the emergence of cognitive abilities thanks to adaptive mechanisms, particularly evolutionary processes. After a renewal of the field more focused on learning, LCSs are now considered as sequential decision problem-solving systems endowe...

Design of multiple classifier systems

Design of Multiple Classifier Systems

In the field of pattern recognition, multiple classifier systems based on the combination of outputs of a set of different classifiers have been proposed as a method for the development of high performance classification systems. In this chapter, the problem of multiple classifier system design is discussed and the reader is provided with a critical survey of the state of the art. A formulation...

Multiple Classifier Systems under Attack

In adversarial classification tasks like spam filtering, intrusion detection in computer networks and biometric authentication, a pattern recognition system must not only be accurate, but also robust to manipulations of input samples made by an adversary to mislead the system itself. It has been recently argued that the robustness of a classifier could be improved by avoiding to overemphasize o...