Leveraging hybrid machine learning for big data challenges in contemporary literary studies

Abstract

Hybrid and ensemble machine learning strategies are gaining particular importance. Their difference lies in the integration of different methods, which allows combining the advantages of several algorithms and neutralizing their limitations. Hybrid systems combine statistical models with deep learning algorithms and natural language processing methods, creating multi-level analytics for humanitarian data. Ensemble approaches (begging, boosting, stacking, etc.) combine the results of several models, ensuring high stability and accuracy of results.