  This study introduces CCNETS (Causal Learning with Causal Cooperative Nets),
a novel generative model-based classifier designed to tackle the challenge of
generating data for imbalanced datasets in pattern recognition. CCNETS is
uniquely crafted to emulate brain-like information processing and comprises
three main components: Explainer, Producer, and Reasoner. Each component is
designed to mimic specific brain functions, which aids in generating
high-quality datasets and enhancing classification performance.
  The model is particularly focused on addressing the common and significant
challenge of handling imbalanced datasets in machine learning. CCNETS's
effectiveness is demonstrated through its application to a "fraud dataset,"
where normal transactions significantly outnumber fraudulent ones (99.83% vs.
0.17%). Traditional methods often struggle with such imbalances, leading to
skewed performance metrics. However, CCNETS exhibits superior classification
ability, as evidenced by its performance metrics. Specifically, it achieved an
F1-score of 0.7992, outperforming traditional models like Autoencoders and
Multi-layer Perceptrons (MLP) in the same context. This performance indicates
CCNETS's proficiency in more accurately distinguishing between normal and
fraudulent patterns.
  The innovative structure of CCNETS enhances the coherence between generative
and classification models, helping to overcome the limitations of pattern
recognition that rely solely on generative models. This study emphasizes
CCNETS's potential in diverse applications, especially where quality data
generation and pattern recognition are key. It proves effective in machine
learning, particularly for imbalanced datasets. CCNETS overcomes current
challenges in these datasets and advances machine learning with brain-inspired
approaches.
