A number of classifier fusion methods have been
recently developed opening an alternative approach
leading to a potential improvement in the
classification performance. As there is little theory of
information fusion itself, currently we are faced with
different methods designed for different problems and
producing different results. This paper gives an
overview of classifier fusion methods and attempts to
identify new trends that may dominate this area of
research in future. A taxonomy of fusion methods
trying to bring some order into the existing “pudding
of diversities” is also provided
