Methods for designing multiple classifier systems
F Roli, G Giacinto, G Vernazza - International Workshop on Multiple …, 2001 - Springer
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 paper, the problem of design of multiple
classifier system is discussed. Six design methods based on the so-called “overproduce and
choose “paradigm are described and compared by experiments. Although these design
methods exhibited some interesting features, they do not guarantee to design the optimal …
outputs of a set of different classifiers have been proposed as a method for the development
of high performance classification systems. In this paper, the problem of design of multiple
classifier system is discussed. Six design methods based on the so-called “overproduce and
choose “paradigm are described and compared by experiments. Although these design
methods exhibited some interesting features, they do not guarantee to design the optimal …
Multiple classifier systems
These proceedings are a record of the Multiple Classifier Systems Workshop, MCS 2009,
held at the University of Iceland, Reykjavik, Iceland in June 2009. Being the eighth in a well-
established series of meetings providing an international forum for the discussion of issues
in multiple classifier system design, the workshop achieved its objective of bringing together
researchers from diverse communities (neural networks, pattern recognition, machine
learning and statistics) concerned with this research topic. From more than 70 submissions …
held at the University of Iceland, Reykjavik, Iceland in June 2009. Being the eighth in a well-
established series of meetings providing an international forum for the discussion of issues
in multiple classifier system design, the workshop achieved its objective of bringing together
researchers from diverse communities (neural networks, pattern recognition, machine
learning and statistics) concerned with this research topic. From more than 70 submissions …
[BOOK][B] Multiple Classifier Systems: Second International Workshop, MCS 2001 Cambridge, UK, July 2-4, 2001 Proceedings
Driven by the requirements of a large number of practical and commercially-portant
applications, the last decade has witnessed considerable advances in p-tern recognition.
Better understanding of the design issues and new paradigms, such as the Support Vector
Machine, have contributed to the development of-proved methods of pattern classi cation.
However, while any performance gains are welcome, and often extremely signi cant from the
practical point of view, it is increasingly more challenging to reach the point of perfection as …
applications, the last decade has witnessed considerable advances in p-tern recognition.
Better understanding of the design issues and new paradigms, such as the Support Vector
Machine, have contributed to the development of-proved methods of pattern classi cation.
However, while any performance gains are welcome, and often extremely signi cant from the
practical point of view, it is increasingly more challenging to reach the point of perfection as …
Sum versus vote fusion in multiple classifier systems
Amidst the conflicting experimental evidence of superiority of one over the other, we
investigate the Sum and majority Vote combining rules in a two class case, under the
assumption of experts being of equal strength and estimation errors conditionally
independent and identically distributed. We show, analytically, that, for Gaussian estimation
error distributions, Sum always outperforms Vote. For heavy tail distributions, we
demonstrate by simulation that Vote may outperform Sum. Results on synthetic data confirm …
investigate the Sum and majority Vote combining rules in a two class case, under the
assumption of experts being of equal strength and estimation errors conditionally
independent and identically distributed. We show, analytically, that, for Gaussian estimation
error distributions, Sum always outperforms Vote. For heavy tail distributions, we
demonstrate by simulation that Vote may outperform Sum. Results on synthetic data confirm …