5 citations · 6 across the 3 of their papers we have counts for
3 papers
cs.LG2023
Balancing Efficiency vs. Effectiveness and Providing Missing Label Robustness in Multi-Label Stream Classification
Sepehr Bakhshi, Fazli Can
Available works addressing multi-label classification in a data stream environment focus on proposing accurate models; however, these models often exhibit inefficiency and cannot b…
cs.LG2023★ 5 cited
DynED: Dynamic Ensemble Diversification in Data Stream Classification
Soheil Abadifard, Sepehr Bakhshi, Sanaz Gheibuni +1
Ensemble methods are commonly used in classification due to their remarkable performance. Achieving high accuracy in a data stream environment is a challenging task considering dis…
cs.LG2023★ 1 cited
Leveraging Linear Independence of Component Classifiers: Optimizing Size and Prediction Accuracy for Online Ensembles
Enes Bektas, Fazli Can
Ensembles, which employ a set of classifiers to enhance classification accuracy collectively, are crucial in the era of big data. However, although there is general agreement that…