B0626
Title: Merged linear Gaussian cluster-weighted models: An interpretable machine learning model
Authors: Sangkon Oh - Sungkyunkwan University (Korea, South) [presenting]
Byungtae Seo - Sungkyunkwan University (Korea, South)
Abstract: Cluster-weighted models (CWMs) are useful tools for identifying latent functional relationships between response variables and covariates. However, owing to excess distributional assumptions made on the covariates, these models can suffer misspecifications of component distributions, which could also undermine the estimation accuracy and render the model structure complicated for interpretation. To address this issue, we consider CWMs with univariate responses and propose a novel CWM by modelling each regression cluster as a finite mixture to enhance flexibility while retaining parsimony. We prove that the proposed method can provide more meaningful regression clusters in the data than those of existing methods and not only has good prediction accuracy when predicting new data but is also interpretable. Additionally, we present a procedure to construct such a proposed CWM and a feasible expectation-maximization algorithm to estimate the model parameters. Numerical demonstrations, including simulations and real data analysis, are also provided.
