Multiclass SVM classification using graphs calibrated by similarity between classes

Authors:
Marcin Luckner
Affiliations:
Warsaw University of Technology, Faculty of Mathematics and Information Science, Warsaw, Poland
Venue:
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part IV
Year:
2011

Citing 5
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Abstract

In this paper new learning structures, similarity between classes based trees and directed acyclic graph, are presented. The proposed structures are based on a distribution of recognized classes in a data space, unlike the known graph methods such as the tree based One-Against-All (OAA) algorithm or the directed acyclic graph based One-Against-One (OAO) algorithm. The structures are created by grouping similar classes. The similarity between classes is estimated by a distance between classes. The OAO strategy is implemented only for the nearest classes. In other cases the OAA strategy is used. This method allows reduction of the classification costs without a significant growth of the classification error. Algorithms, which create similarity based trees and directed acyclic graph are presented in this paper. These methods are also compared in digits recognition task with existing ones.