Simple supervised dissimilarity measure: Bolstering iForest-induced similarity with class information without learning

摘要

Existing distance metric learning methods require optimisation to learn a feature space to transform data—this makes them computationally expensive in large datasets. In classification tasks, they make use of class information to learn an appropriate feature space. In this paper, we present a simple supervised dissimilarity measure which does not require learning or optimisation. It uses class information to measure dissimilarity of two data instances in the input space directly. It is a supervised version of an existing data-dependent dissimilarity measure called \(m_\mathrm{e}\). Our empirical results in k-NN and LVQ classification tasks show that the proposed simple supervised dissimilarity measure generally produces predictive accuracy better than or at least as good as existing state-of-the-art supervised and unsupervised dissimilarity measures.