Transformation of Relational Features for Use with Conventional Classifiers

Abstract

In this paper, we address the problem of transforming relational features into an Euclidian space so that standard classification methods that assume that data is in a vector form could be used. Our approach has three main steps. First, a relational matrix that represents the pair-wise dissimilarities between all objects is constructed. Second, a fuzzy relational clustering algorithm is used to partition the data into groups of similar objects. Third, the relational data features are mapped to a unit hyper-cube space where each object is represented by its membership vectors in all clusters. The proposed method is validated by comparing the performance of several classifiers with different feature sets on the original and the transformed spaces. We show that the transformed space conserves the discriminative information of the original features. We also show that, using the transformed space, a richer set of standard classifiers could be used.