A personalized classification model using similarity learning via supervised autoencoder

Abstract

Personalized modeling usually trains a predictive model for a new point using only observations similar to the new point. However, existing methodologies have limitations that do not reflect the target variable in the similarity calculation nor the density of neighbors. Thus, this paper proposes a new personalized modeling method. The proposed methodology transforms the input variables into the latent variables through a supervised autoencoder and calculates the similarity measure between observations in the transformed latent space. The proposed method also considers the neighborhood density around the test point. As a result of the experiments with real datasets, it was found that the proposed method outperformed other benchmark methods and showed the interpretability of the predictive model.