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arXiv · 2603.10049

InFusionLayer: a CFA-based ensemble tool to generate new classifiers for learning and modeling

Eric Roginek·Jingyan Xu·D. Frank. Hsu
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Abstract

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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Eric Roginek, Jingyan Xu, D. Frank. Hsu. 2026-03-09. InFusionLayer: a CFA-based ensemble tool to generate new classifiers for learning and modeling. https://arxiv.org/abs/2603.10049

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