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.

翻译：本研究提出CCNETS（基于因果协作网络的因果学习），这是一种新型的生成式模型分类器，旨在解决模式识别中面向不平衡数据集生成数据的挑战。CCNETS独特地模拟类脑信息处理过程，包含三大核心组件：解释器（Explainer）、生成器（Producer）与推理器（Reasoner）。每个组件均被设计为模拟特定脑功能，从而辅助生成高质量数据集并提升分类性能。该模型特别聚焦于机器学习中处理不平衡数据集这一普遍且重大的挑战。通过应用于"欺诈数据集"（其中正常交易占比99.83%而欺诈交易仅占0.17%），CCNETS的有效性得到验证。传统方法常因处理此类不平衡问题导致性能指标偏差，而CCNETS展现了卓越的分类能力——其F1分数达到0.7992，在相同场景下优于自编码器（Autoencoders）与多层感知机（MLP）等传统模型。这一表现表明CCNETS能更精准地区分正常与欺诈模式。其创新结构增强了生成模型与分类模型之间的协同性，有助于突破仅依赖生成模型的模式识别局限。本研究强调了CCNETS在需要高质量数据生成与模式识别的多样化应用场景中的潜力，在机器学习领域尤其对不平衡数据集处理表现优异。CCNETS通过脑启发式方法克服了当前数据集处理面临的挑战，推动了机器学习的发展。