Polychotomous classification with pairwise classifiers: a new voting principle

Authors:
Florin Cutzu
Affiliations:
Dept. of Computer Science, Indiana University, Bloomington, IN
Venue:
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Year:
2003

Citing 3
Cited 4

Pattern classification: a unified view of statistical and neural approaches

Pattern classification: a unified view of statistical and neural approaches
Classification by pairwise coupling

NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers

ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning

Efficient Pairwise Classification

ECML '07 Proceedings of the 18th European conference on Machine Learning
Combining predictions in pairwise classification: An optimal adaptive voting strategy and its relation to weighted voting

Pattern Recognition
Fuzzy pairwise multiclass support vector machines

MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
Efficient prediction algorithms for binary decomposition techniques

Data Mining and Knowledge Discovery

Quantified Score

Hi-index	0.00

Visualization

Abstract

A new principle for performing polychotomous classification with pairwise classifiers is introduced: if pairwise classifier Nij, trained to discriminate between classes i and j, responds "i" for an input x from an unknown class (not necessarily i or j), one can at best conclude that x ∉ j. Thus, the output of pairwise classifier Nij can be interpreted as a vote against the losing class j, and not, as existing methods propose, as a vote for the winning class i. Both a discrete and a continuous classification model derived from this principle are introduced.