Classifier Chain Algorithm Improvements: Recent Research (2019-2021)

The Classifier Chain (CC) algorithm is a popular approach for multi-label classification. Recent research has focused on improving the performance and efficiency of CC by incorporating novel techniques. Here are some key advancements published between 2019 and 2021:

'A multi-label classification framework based on deep learning and chain classifiers' by Y. Zhang, L. Cheng, and L. Zhu, published in Neurocomputing in 2021. This work proposes a deep learning-based framework for multi-label classification that leverages the strengths of chain classifiers.
'Enhanced Classifier Chains for Multi-Label Classification Using Hybridized Feature Selection and Feature Weighting Techniques' by S. R. Sankar and S. K. Kopparthi, published in IEEE Access in 2020. This paper introduces a novel approach to enhance CC by integrating feature selection and feature weighting techniques.
'Multi-Label Classification Using A Bayesian Framework and A Hybrid Chain Algorithm' by S. Saha and S. Mondal, published in the Proceedings of the 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) in 2020. This research explores the use of a Bayesian framework and a hybrid chain algorithm for multi-label classification.
'Multi-Label Classification Based on Ensemble Classifier Chain with Semi-Supervised Learning' by M. P. Thakur and V. K. Singh, published in the Proceedings of the 2019 International Conference on Intelligent Systems Design and Applications (ISDA) in 2019. This work proposes an ensemble approach to CC that incorporates semi-supervised learning techniques.
'Multi-Label Classification Using Classifier Chains and Label Embeddings' by S. Saha and S. Mondal, published in the Proceedings of the 2019 International Conference on Computational Intelligence and Computing Research (ICCIC) in 2019. This paper explores the use of label embeddings in conjunction with CC for multi-label classification.

These recent publications highlight the ongoing research efforts to improve the performance and efficiency of Classifier Chains for multi-label classification. The use of deep learning, feature selection, Bayesian frameworks, and ensemble methods are demonstrating significant progress in this field.