Output details
15 - General Engineering
University of Kent
Diversity in multiple classifier ensembles based on binary feature quantisation with application to face recognition
While the benefits from combining multiple classifiers in ensembles have been established by a large number of empirical studies, there are only a handful of papers examining the requirements for forming ensembles which outperform constituent classifiers. This paper, a major output from an EPSRC project (GR/S24831/01), offers a novel analysis of “diversity” which is recognized as one of the most important issues for the development of accurate multi-classifier systems. Our results strongly suggest that increased diversity among the constituent classifiers is not sufficient to guarantee performance improvements; rather a trade-off between participant classifiers’ accuracy and diversity is necessary to achieve maximum gains.