Application Research of Multi-label Learning Under Concept Drift
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-7502-0_44
Reference11 articles.
1. Halstead B, Koh YS, Riddle P, Pechenizkiy M, Bifet A (2023) Combining diverse meta-features to accurately identify recurring concept drift in data streams. ACM Trans Knowl Discov Data 17(8):1–36
2. Han M, Chen Z, Li M, Wu H, Zhang X (2022) A survey of active and passive concept drift handling methods. Comput Intell 38(4):1492–1535
3. Qian K, Min XY, Cheng Y, Min F (2023) Weight matrix sharing for multi-label learning. Pattern Recogn 136:109156
4. Zhang ML, Zhou ZH (2007) ML-KNN: a lazy learning approach to multi-label learning. Pattern Recogn 40(7):2038–2048
5. Xu X, Shan D, Li S, Sun T, Xiao P, Fan J (2019) Multi-label learning method based on ML-RBF and laplacian ELM. Neurocomputing 331:213–219
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