5 citations · 5 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.LG2021
A Broad Ensemble Learning System for Drifting Stream Classification
Sepehr Bakhshi, Pouya Ghahramanian, Hamed Bonab +1
In a data stream environment, classification models must handle concept drift efficiently and effectively. Ensemble methods are widely used for this purpose; however, the ones avai…