Bayesian ART for incomplete datasets

doi:10.1016/j.asoc.2024.111865

Bayesian ART for incomplete datasets

2024-09-01

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AI

Ajalmar R. Rocha Neto

D

Diego Mesquita

DOI：10.1016/j.asoc.2024.111865

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Abstract

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Adaptive Resonance Theory (ART) models allow for categorizing data in a fast and incremental manner. In particular, the Bayesian ART leverages Bayesian methodology to capture complex relationships between categories. While Bayesian methods are well-known for handling uncertainty, Bayesian ART models are at the mercy of a recurring foe: missing data. When data is missing, practitioners must rely on off -the -shelf solutions to impute the data before using Bayesian ART, effectively truncating the uncertainty quantification. To overcome such limitation, we (I) estimate the distribution of missing data entries using a Gaussian mixture model and (II) modify the three steps of Bayesian ART (category choice, matching, and update) to propagate the uncertainty around the missing entries. Experiments in a variety of tabular datasets show that, in general, our novel methodology leads to better results than using off -the -shelf imputation solutions. The performance gap becomes especially noticeable as the number of missing data entries increases.

Keywords:

Adaptive Resonance Theory

Bayesian ART

Missing values

Bayesian ART for incomplete datasets

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