Feature Ranking Algorithms for Improving Classification of Vector Space Embedded Graphs
Graphs provide us with a powerful and flexible representation formalism for pattern recognition. Yet, the vast majority of pattern recognition algorithms rely on vectorial data descriptions and cannot directly be applied to graphs. In order to overcome this severe limitation, an embedding of the underlying graphs in a vector space ℝn is employed. The basic idea is to regard the dissimilarities of a graph g to a number of prototype graphs as numerical features of g. In previous works, the prototypes are selected beforehand with selection strategies based on some heuristics. In the present paper we take a more fundamental approach and regard the problem of prototype selection as a feature selection problem, for which many methods are available. With several experimental results we show the feasibility of graph embedding based on prototypes obtained from feature selection algorithms.
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