Markov model

Oversampling

Bandwidth (computing)

Construct (python library)

Computer network

Mathematics

Variable-order Markov model

Markov chain

Causal model

Data mining

Statistics

Machine learning

Markov blanket

Estimator

Artificial intelligence

Programming language

Class (philosophy)

Geometry

Constraint (computer-aided design)

Computer science

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.

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