Binary Transformation Method for Multi-Label Stream Classification

Gulcan, Ege Berkay; Ecevit, Isin Su; Can, Fazlı

doi:10.1145/3511808.3557553

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Other1

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Balancing efficiency vs. effectiveness and providing missing label robustness in multi-label stream classification

Bakhshi,

Can

2024

Knowledge-Based Systems

View full text Add to dashboard Cite

Balancing efficiency vs. effectiveness and providing missing label robustness in multi-label stream classification

Bakhshi,

Can

2024

Knowledge-Based Systems

View full text Add to dashboard Cite

Prioritized Binary Transformation Method for Efficient Multi-label Classification of Data Streams with Many Labels

Yildirim,

Bakhshi,

Can

2024

Proceedings of the 33rd ACM International Conference on Information and Knowledge Management

View full text Add to dashboard Cite

Imbalance-Robust Multi-Label Self-Adjusting kNN

Nicola,

Delgado,

Lauretto

2024

ACM Trans. Knowl. Discov. Data

View full text Add to dashboard Cite

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.

show abstract

Binary Transformation Method for Multi-Label Stream Classification

Cited by 3 publications

References 22 publications

Balancing efficiency vs. effectiveness and providing missing label robustness in multi-label stream classification

Balancing efficiency vs. effectiveness and providing missing label robustness in multi-label stream classification

Prioritized Binary Transformation Method for Efficient Multi-label Classification of Data Streams with Many Labels

Imbalance-Robust Multi-Label Self-Adjusting kNN

Contact Info

Product

Resources

About