Enhancing Sentiment Classification with Machine Learning and Combinatorial Fusion
This addresses sentiment analysis for natural language processing applications, offering an efficient alternative to scaling model size, though it appears incremental as it builds on existing ensemble and model integration techniques.
The paper tackles sentiment classification by applying Combinatorial Fusion Analysis to integrate an ensemble of diverse machine learning models, achieving state-of-the-art accuracy of 97.072% on the IMDB dataset.
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.