Method for Training Perceptron on Tabular Data with Missing Values

Abstract

Handling missing values in tabular data remains a critical challenge for building robust machine learning models. This paper presents a novel approach to imputation based on unary classification. The proposed method employs an ensemble of perceptrons trained independently for each class to estimate the likelihood of reconstructed values with respect to the empirical support of that class. A uniform distribution over a bounded region of the feature space is used as a background model, enabling the interpretation of the model’s output as an approximation of the posterior probability that an object belongs to a given class. This probabilistic interpretation is then leveraged within an iterative procedure for missing value imputation and classifier training. The theoretical validity of the proposed estimator is rigorously justified. Experiments on synthetic two-dimensional datasets with missing values generated under the MCAR (Missing Completely At Random) mechanism demonstrate the superiority of the proposed method over classical imputation techniques, particularly in scenarios with high missingness rates and complex class boundaries.