Balancing efficiency vs. effectiveness and providing missing label robustness in multi-label stream classification
Available works addressing multi-label classification in a data stream environment focus on
proposing accurate prediction models; however, they struggle to balance effectiveness and …
proposing accurate prediction models; however, they struggle to balance effectiveness and …
Prioritized Binary Transformation Method for Efficient Multi-label Classification of Data Streams with Many Labels
Real-time data processing systems generate huge amounts of data that need to be
classified. The volume, variety, velocity, and veracity (uncertainty) of this data necessitate …
classified. The volume, variety, velocity, and veracity (uncertainty) of this data necessitate …
Imbalance-Robust Multi-Label Self-Adjusting kNN
VGOM Nicola, KV Delgado, MS Lauretto - ACM Transactions on …, 2024 - dl.acm.org
In the task of multi-label classification in data streams, instances arriving in real time need to
be associated with multiple labels simultaneously. Various methods based on the k Nearest …
be associated with multiple labels simultaneously. Various methods based on the k Nearest …
Enhancing Multi-Label Text Classification by Incorporating Label Dependency to Handle Imbalanced Data
Multi-label text classification (MLTC) holds significant importance in the field of data
management and information retrieval, where the distribution of label samples often exhibits …
management and information retrieval, where the distribution of label samples often exhibits …
Online Multi-Label Classification under Noisy and Changing Label Distribution
Y Zou, X Hu, P Li, J Hu, Y Wu - arXiv preprint arXiv:2410.02394, 2024 - arxiv.org
Multi-label data stream usually contains noisy labels in the real-world applications, namely
occuring in both relevant and irrelevant labels. However, existing online multi-label …
occuring in both relevant and irrelevant labels. However, existing online multi-label …