||Author(s): G. Tsoumakas, L. Angelis, I. Vlahavas.
Title: “Selective Fusion of Heterogeneous Classifiers”.
Click here to download the PDF (Acrobat Reader) file (28 pages).
Intelligent Data Analysis, IOS Press, 9(6), pp. 511-525, 2005.
Abstract: There are two main paradigms in combining different classification
algorithms: Classifier Selection and Classifier Fusion. The first
one selects a single model for classifying a new instance, while
the latter combines the decisions of all models. The work
presented in this paper stands in between these two paradigms
aiming tackle the disadvantages and benefit from the advantages of
both. In particular, this paper proposes the use of statistical
procedures for the selection of the best subgroup among different
classification algorithms and the subsequent fusion of the
decision of the models in this subgroup with simple methods like
Weighted Voting. Extensive experimental results show that the
proposed approach, Selective Fusion, improves over simple
selection and fusion methods, leading to performance comparable
with the state-of-the-art heterogeneous classifier combination
method of Stacking, without the additional computational cost and
learning problems of meta-training.