A personalized classification model using similarity learning via supervised autoencoder

• Published in: Applied soft computing 2022-12, Vol.131, p.109773, Article 109773
• Main Authors: Jo, Hyunjae, Jun, Chi-Hyuck
• Format: Article
• Language: English
• Subjects: Classification; Latent variables; Personalized model; Similarity learning; Supervised autoencoder
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2022.109773

Personalized modellingmodelingusually 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 modellingmodelingmethod. 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. •Trains predictive models for new observations using the sampled neighbor observations.•Proposes a new personalized modeling method through a supervised autoencoder.•Transforms the input variables into the latent variables and calculates the similarity measures.•Proposed method outperformed other benchmark methods and showed the interpretability.