67 citations · 155 across the 13 of their papers we have counts for
13 papers
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…
Stance Detection and Open Research Avenues
Dilek Küçük, Fazli Can
This tutorial aims to cover the state-of-the-art on stance detection and address open research avenues for interested researchers and practitioners. Stance detection is a recent re…
MiDe22: An Annotated Multi-Event Tweet Dataset for Misinformation Detection
Cagri Toraman, Oguzhan Ozcelik, Furkan Şahinuç +1
The rapid dissemination of misinformation through online social networks poses a pressing issue with harmful consequences jeopardizing human health, public safety, democracy, and t…
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 Tweet Dataset Annotated for Named Entity Recognition and Stance Detection
Dilek Küçük, Fazli Can
Annotated datasets in different domains are critical for many supervised learning-based solutions to related problems and for the evaluation of the proposed solutions. Topics in na…
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…
Multilingual Sentiment Analysis: An RNN-Based Framework for Limited Data
Ethem F. Can, Aysu Ezen-Can, Fazli Can
Sentiment analysis is a widely studied NLP task where the goal is to determine opinions, emotions, and evaluations of users towards a product, an entity or a service that they are…
Stance Detection on Tweets: An SVM-based Approach
Dilek Küçük, Fazli Can
Stance detection is a subproblem of sentiment analysis where the stance of the author of a piece of natural language text for a particular target (either explicitly stated in the t…
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…