Multi-Label Classification Algorithm for Adaptive Heterogeneous Classifier Group by Meng Han, Shurong Yang, Hongxin Wu, Jian Ding

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A novel multi-label classification algorithm based on -nearest neighbor and random walk by Zhen-Wu Wang, Si-Kai Wang, Ben-Ting Wan, William Wei Song

“…One challenge of using the random walk-based multi-label classification algorithms is to construct a random walk graph for the multi-label classification algorithms, which may lead to poor classification quality and high algorithm complexity. …”
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A multi-label classification method for disposing incomplete labeled data and label relevance by Lina ZHANG, Lingpeng DAI, Tai KUANG

“…Multi-label classification methods have been applied in many real-world fields，in which the labels may have strong relevance and some of them even are incomplete or missing.However，existing multi-label classification algorithms are unable to handle both issues simultaneously.A new probabilistic model that can automatically learn and exploit multi-label relevance was proposed on label relevance and missing label classification simultaneously.By integrating out the missing information，it also provides a disciplined approach to handle missing labels.Experiments on a number of real world data sets with both complete and incomplete labels demonstrated that the proposed method can achieve higher classification and prediction evaluation scores than the existing multi-label classification algorithms.…”
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Risk Measurement of TAVR Surgical Complications Based on Unbalanced Multilabel Classification Approaches by Yue Zhang, Yuantao Xie

“…In the empirical analysis, with real surgical data from a hospital in the United States, a combination of multi-label random sampling and representative multi-label classification algorithms was used to fit the data. The model was compared across multiple evaluation metrics, including Hamming loss, ranking loss, and micro-AUC, to ensure robust results. …”
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