67 citations · 155 across the 13 of their papers we have counts for
8 papers · 1 filter
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…
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…
Implicit Concept Drift Detection for Multi-label Data Streams
Ege Berkay Gulcan, Fazli Can
Many real-world applications adopt multi-label data streams as the need for algorithms to deal with rapidly changing data increases. Changes in data distribution, also known as con…
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…
On-the-Fly Ensemble Pruning in Evolving Data Streams
Sanem Elbasi, Alican Büyükçakır, Hamed Bonab +1
Ensemble pruning is the process of selecting a subset of componentclassifiers from an ensemble which performs at least as well as theoriginal ensemble while reducing storage and co…
A Novel Online Stacked Ensemble for Multi-Label Stream Classification
Alican Büyükçakır, Hamed Bonab, Fazli Can
As data streams become more prevalent, the necessity for online algorithms that mine this transient and dynamic data becomes clearer. Multi-label data stream classification is a su…
GOOWE: Geometrically Optimum and Online-Weighted Ensemble Classifier for Evolving Data Streams
Hamed R. Bonab, Fazli Can
Designing adaptive classifiers for an evolving data stream is a challenging task due to the data size and its dynamically changing nature. Combining individual classifiers in an on…
Less Is More: A Comprehensive Framework for the Number of Components of Ensemble Classifiers
Hamed Bonab, Fazli Can
The number of component classifiers chosen for an ensemble greatly impacts the prediction ability. In this paper, we use a geometric framework for a priori determining the ensemble…