基于CFA的集成学习工具，提升多分类模型性能
InFusionLayer: a CFA-based ensemble tool to generate new classifiers for learning and modeling
- 采用秩评分函数与认知多样性融合基础模型
- 在多个视觉数据集上验证性能优于单一模型
- 支持PyTorch/TensorFlow/Scikit-learn，易用性强
集成学习是通过组合多个算法/模型来提升预测性能的经典方法。组合式融合分析（CFA）通过秩评分特征（RSC）函数和认知多样性（CD）提供了融合多个评分系统的理论与实践框架。然而，目前尚无通用的Python工具实现这些技术。本文提出InFusionLayer，一种受CFA启发的系统级融合机器学习架构，利用少量基础模型优化无监督与有监督的多分类任务。我们通过在多个计算机视觉数据集上的验证，展示了InFusionLayer在PyTorch、TensorFlow和Scikit-learn工作流中的易用性与高效性。结果表明，融合RSC函数与认知多样性的特性显著提升了性能，为更复杂的集成学习应用铺平道路。代码已开源，可在GitHub获取：https://github.com/ewroginek/Infusion
原文摘要 · Abstract (English)
Ensemble learning is a well established body of methods for machine learning to enhance predictive performance by combining multiple algorithms/models. Combinatorial Fusion Analysis (CFA) has provided method and practice for combining multiple scoring systems, using rank-score characteristic (RSC) function and cognitive diversity (CD), including ensemble method and model fusion. However, there is no general-purpose Python tool available that incorporate these techniques. In this paper we introduce \texttt{InFusionLayer}, a machine learning architecture inspired by CFA at the system fusion level that uses a moderate set of base models to optimize unsupervised and supervised learning multiclassification problems. We demonstrate \texttt{InFusionLayer}'s ease of use for PyTorch, TensorFlow, and Scikit-learn workflows by validating its performance on various computer vision datasets. Our results highlight the practical advantages of incorporating distinctive features of RSC function and CD, paving the way for more sophisticated ensemble learning applications in machine learning. We open-sourced our code to encourage continuing development and community accessibility to leverage CFA on github: https://github.com/ewroginek/Infusion
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