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.

CCNETS 是一种基于生成模型的分类器，针对模式识别中不平衡数据集的数据生成挑战，通过模拟大脑信息处理的构造，有效地生成高质量数据集并提高分类性能。在应用于 “欺诈数据集” 时，CCNETS 表现出卓越的分类能力，通过 F1 分数达到 0.7992，优于传统模型如自编码器和多层感知机。CCNETS 在数据生成和模式识别方面具有巨大的潜力，并在机器学习中克服了当前数据集所面临的挑战，推动以脑启发方法的机器学习的进展。