Comparing Combination Rules of Pairwise Neural Networks Classifiers |
| |
Authors: | Olivier Lézoray Hubert Cardot |
| |
Affiliation: | (1) GREYC UMR CNRS 6072, Université de Caen, 6 Bd. Maréchal Juin, Caen, 14050, France;(2) Laboratoire d’Informatique EA 2101, Université Francois-Rabelais de Tours, 64 Avenue Jean Portalis, Tours, 37200, France |
| |
Abstract: | A decomposition approach to multiclass classification problems consists in decomposing a multiclass problem into a set of
binary ones. Decomposition splits the complete multiclass problem into a set of smaller classification problems involving
only two classes (binary classification: dichotomies). With a decomposition, one has to define a recombination which recomposes
the outputs of the dichotomizers in order to solve the original multiclass problem. There are several approaches to the decomposition,
the most famous ones being one-against-all and one-against-one also called pairwise. In this paper, we focus on pairwise decomposition
approach to multiclass classification with neural networks as the base learner for the dichotomies. We are primarily interested
in the different possible ways to perform the so-called recombination (or decoding). We review standard methods used to decode
the decomposition generated by a one-against-one approach. New decoding methods are proposed and compared to standard methods.
A stacking decoding is also proposed which consists in replacing the whole decoding or a part of it by a trainable classifier
to arbiter among the conflicting predictions of the pairwise classifiers. Proposed methods try to cope with the main problem
while using pairwise decomposition: the use of irrelevant classifiers. Substantial gain is obtained on all datasets used in
the experiments. Based on the above, we provide future research directions which consider the recombination problem as an
ensemble method. |
| |
Keywords: | Pairwise classifier Combination Stacking |
本文献已被 SpringerLink 等数据库收录! |
|