An Efficient Framework for Multi-Label Learning in Non-stationary Data Stream

Xiu-lin, Zheng; Li, Peipei

doi:10.1109/ickg52313.2021.00029

2021 IEEE International Conference on Big Knowledge (ICBK) 2021

DOI: 10.1109/ickg52313.2021.00029

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An Efficient Framework for Multi-Label Learning in Non-stationary Data Stream

Zheng Xiu-lin

Peipei Li

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Cited by 2 publications

References 31 publications

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A Weighted Ensemble Classification Algorithm Based on Nearest Neighbors for Multi-Label Data Stream

Han

Chen

et al. 2023

ACM Trans. Knowl. Discov. Data

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With the rapid development of data stream, multi-label algorithms for mining dynamic data become more and more important. At the same time, when data distribution changes, concept drift will occur, which will make the existing classification models lose effectiveness. Ensemble methods have been used for multi-label classification, but few methods consider both the accuracy and diversity of base classifiers. To address the above-mentioned problem, a Weighted Ensemble classification algorithm based on Nearest Neighbors for Multi-Label data stream (WENNML) is proposed. WENNML uses data blocks to train Active candidate Ensemble Classifiers (AEC) and Passive candidate Ensemble Classifiers (PEC). The base classifiers of AEC and PEC are dynamically updated using geometric and diversity weighting methods. When the difference value between the number of current instances and the number of warning instances reaches the passive warning value, the algorithm selects the optimal base classifiers from AEC and PEC according to the subset accuracy and hamming score and puts them into the predictive ensemble classifiers. Experiments are carried out on 12 kinds of datasets with 9 comparison algorithms. The results show that WENNML achieves the best average rankings among the four evaluation metrics.

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A Weighted Ensemble Classification Algorithm Based on Nearest Neighbors for Multi-Label Data Stream

Han

Chen

et al. 2023

ACM Trans. Knowl. Discov. Data

View full text Add to dashboard Cite

show abstract

Imbalance-Robust Multi-Label Self-Adjusting kNN

Nicola,

Delgado,

Lauretto

2024

ACM Trans. Knowl. Discov. Data

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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 Neighbors algorithm have been proposed to address this task. However, these methods face limitations when dealing with imbalanced data streams, a problem that has received limited attention in existing works. To approach this gap, this paper introduces the Imbalance-Robust Multi-Label Self-Adjusting kNN (IRMLSAkNN), designed to tackle multi-label imbalanced data streams. IRMLSAkNN’s strength relies on maintaining relevant instances with imbalance labels by using a discarding mechanism that considers the imbalance ratio per label. On the other hand, it evaluates subwindows with an imbalance-aware measure to discard older instances that are lacking performance. We conducted statistical experiments on 32 benchmark data streams, evaluating IRMLSAkNN against eight multi-label classification algorithms using common accuracy-aware and imbalance-aware measures. The obtained results demonstrate that IRMLSAkNN consistently outperforms these algorithms in terms of predictive capacity and time cost across various levels of imbalance.

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An Efficient Framework for Multi-Label Learning in Non-stationary Data Stream

Cited by 2 publications

References 31 publications

A Weighted Ensemble Classification Algorithm Based on Nearest Neighbors for Multi-Label Data Stream

A Weighted Ensemble Classification Algorithm Based on Nearest Neighbors for Multi-Label Data Stream

Imbalance-Robust Multi-Label Self-Adjusting kNN

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