InFusionLayer: A CFA-Based Ensemble Tool to Generate New Classifiers for Learning and Modeling

2024-10-28

IEEE International Conference on Tools with Artificial Intelligence

AI Summary

This work addresses the lack of a unified Python framework for ensemble learning methods grounded in Composite Fusion Analysis (CFA), particularly in integrating Rank-Score Characteristic (RSC) functions with Cognitive Diversity (CD). To bridge this gap, we propose InFusionLayer—a general-purpose machine learning architecture inspired by CFA that, for the first time, unifies RSC and CD mechanisms within a single framework compatible with PyTorch, TensorFlow, and Scikit-learn. Requiring only a small set of base models, our approach achieves substantial performance gains in both unsupervised and supervised multi-class classification tasks. Extensive experiments across multiple computer vision benchmarks validate its efficacy, and the open-sourced implementation facilitates the practical adoption and broader dissemination of CFA within mainstream deep learning ecosystems.

Technology Category

• Machine Learning: Ensemble Methods
• Computer Vision: Representation Learning for Vision
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

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 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 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

Problem

• Ensemble learning
• Combinatorial Fusion Analysis
• Rank-score characteristic
• Cognitive diversity
• Classifier generation

Innovation

• Combinatorial Fusion Analysis
• Ensemble Learning
• Rank-Score Characteristic
• Cognitive Diversity
• Model Fusion