用组合融合提升情感分类，准确率达97.072%。
Enhancing Sentiment Classification with Machine Learning and Combinatorial Fusion
- 通过认知多样性量化模型差异，智能融合多个模型预测
- 在IMDB数据集上达到97.072%准确率，优于传统集成方法
- 适合追求高效高精度情感分析的研究者与工程师
本文提出一种基于组合融合分析（CFA）的新方法，通过整合多种机器学习模型实现情感分类， 在IMDB情感分析数据集上取得97.072%的准确率。CFA利用认知多样性概念，采用排名得分特征函数量化模型间差异，并战略性融合其预测结果。该方法不依赖扩大单个模型规模，因此在计算资源使用上更高效。实验表明，相比传统集成方法，CFA能更有效利用模型多样性。本研究结合了基于RoBERTa架构的Transformer模型与随机森林、SVM、XGBoost等传统机器学习模型。
原文摘要 · Abstract (English)
This paper presents a novel approach to sentiment classification using the application of Combinatorial Fusion Analysis (CFA) to integrate an ensemble of diverse machine learning models, achieving state-of-the-art accuracy on the IMDB sentiment analysis dataset of 97.072\%. CFA leverages the concept of cognitive diversity, which utilizes rank-score characteristic functions to quantify the dissimilarity between models and strategically combine their predictions. This is in contrast to the common process of scaling the size of individual models, and thus is comparatively efficient in computing resource use. Experimental results also indicate that CFA outperforms traditional ensemble methods by effectively computing and employing model diversity. The approach in this paper implements the combination of a transformer-based model of the RoBERTa architecture with traditional machine learning models, including Random Forest, SVM, and XGBoost.
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