Multiple Classifier Systems: Software Engineered, Automatically Modular Leading to a Taxonomic Overview

Autor: Derek Partridge, Niall Griffith
Rok vydání: 2002
Předmět:
Zdroj: Pattern Analysis & Applications. 5:180-188
ISSN: 1433-755X
1433-7541
DOI: 10.1007/s100440200016
Popis: One vigorous branch of research aimed at improving the performance of pattern recognition systems explores the possibilities for exploiting the differences between a set of variously configured classifiers. This is the field of Multiple Classifier Systems (MCS), and it is based on the premise that it ought to be possible to organise and exploit the strengths and weaknesses of individual classifiers such that the MCS performance is superior to that of any of its components. Important concerns are the efficiency of multiple classifier construction, and the effectiveness of the final MCS. What property or properties of the set of multiple classifiers are being exploited by the various decision strategies, and how are the desired properties to be realised within a set of classifiers? Analogous ideas and strands of research have arisen within both software engineering and neural computing. This paper surveys these other two fields from an MCS perspective with the goal of revealing useful results that should have direct application for current work in MCS. In particular, the survey opens up new possibilities within MCS as well as provides new formal bases for the central underlying ideas, such as classifier independence and diversity. The exploration of diversity is extended to a consideration of MCSs in which the component classifiers are specialised for classification of an identifiable subset of the complete classification problem. Results are given of an empirical study of an automatic specialisation strategy that demonstrates the predictive use of several diversity measures. Finally, a taxonomy is presented as a unifying framework for the many varieties of MCSs.
Databáze: OpenAIRE
Externí odkaz: