Classifier Combination Using Random Walks on the Space of Concepts

Abstract

Abstract. We propose a novel approach for the combination of classifiers based on two commonly adopted strategies in multiclass classification: one-vs-all and one-vs-one. The method relies on establishing the relevance of nodes in a graph defined in the space of concepts. Following a similar approach as in the ranking of websites, the relative strength of the nodes is given by the stationary distribution of a Markov chain defined on that graph. The proposed approach do not requires the base classifiers to provide calibrated probabilities. Experiments on the challenging problem of multiclass image classification show the potentiality of our approach.

A hierarchical propelled fusion strategy for SAR automatic target recognition

“…Thus many new theories in pattern recognition are gradually introduced to MCF. For example, Jorge Sánchez and Javier Redolfi propose a novel approach for the combination of classifiers based on a graph defined in the space of concepts and a Markov chain defined on that graph [10]. Some researchers consider the contributions of various types of feature sets and classifiers, so weights are given to features or classifiers [11][12][13].…”