Oversampling by a Constraint-Based Causal Network in Medical Imbalanced Data Classification

Abstract

A key challenge of oversampling in medical imbalanced data classification is that the generation of new minority samples often neglects rich causal dependencies among features, with each being responsible for disease diagnosis. This leads us to define a constraint-based approach that generates new samples by explicitly discovering and leveraging the inherent local causal variability of features under a global view. Our approach employs causal Markov property to construct a causal network that explicitly characterizes these unique causal configurations of a particular disease as a variable number of nodes and links. By perturbing those learned causal features from majority class, we synthesize new samples in the territory of minority space. An additional sample selection estimator is introduced to choose the most representative samples. Empirical evaluations on four medical datasets suggest our approach significantly outperforms the state-of-the-art methods.

Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward

“…The approaches range from simply utilising existing solutions to designing new techniques. However, many works have focused on tabulated medical data [152][153][154][155] , which is easier to deal with compared to medical images.…”