Search Results for author:
Found 14 papers, 6 papers with code
Balancing Efficiency vs. Effectiveness and Providing Missing Label Robustness in Multi-Label Stream Classification
Our results demonstrate that the proposed approach ML-BELS is successful in balancing effectiveness and efficiency, and is robust to missing labels and concept drift.
Leveraging Linear Independence of Component Classifiers: Optimizing Size and Prediction Accuracy for Online Ensembles
Incorporating real and synthetic datasets, our empirical results demonstrate a trend: increasing the number of classifiers enhances accuracy, as predicted by our theoretical insights.
DynED: Dynamic Ensemble Diversification in Data Stream Classification
A greater diversity of ensemble components is known to enhance prediction accuracy in such settings.
Stance Detection and Open Research Avenues
This tutorial aims to cover the state-of-the-art on stance detection and address open research avenues for interested researchers and practitioners.
Not Good Times for Lies: Misinformation Detection on the Russia-Ukraine War, COVID-19, and Refugees
Misinformation spread in online social networks is an urgent-to-solve problem having harmful consequences that threaten human health, public safety, economics, and so on.
Implicit Concept Drift Detection for Multi-label Data Streams
Many real-world applications adopt multi-label data streams as the need for algorithms to deal with rapidly changing data increases.
A Broad Ensemble Learning System for Drifting Stream Classification
It employs an ensemble of output layers to address the limitations of BLS and handle drifts.
On-the-Fly Ensemble Pruning in Evolving Data Streams
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 computational costs. Ensemble pruning in data streams is a largely unexplored area ofresearch.
A Tweet Dataset Annotated for Named Entity Recognition and Stance Detection
Annotated datasets in different domains are critical for many supervised learning-based solutions to related problems and for the evaluation of the proposed solutions.
A Novel Online Stacked Ensemble for Multi-Label Stream Classification
As data streams become more prevalent, the necessity for online algorithms that mine this transient and dynamic data becomes clearer.
Multilingual Sentiment Analysis: An RNN-Based Framework for Limited Data
Our goal is to build a single model in the language with the largest dataset available for the task, and reuse it for languages that have limited resources.
Stance Detection on Tweets: An SVM-based Approach
The results indicate that joint use of the features based on unigrams, hashtags, and named entities by SVM classifiers is a plausible approach for stance detection problem on sports-related tweets.
Less Is More: A Comprehensive Framework for the Number of Components of Ensemble Classifiers
In this paper, we use a geometric framework for a priori determining the ensemble size, which is applicable to most of existing batch and online ensemble classifiers.
GOOWE: Geometrically Optimum and Online-Weighted Ensemble Classifier for Evolving Data Streams
We propose a novel data stream ensemble classifier, called Geometrically Optimum and Online-Weighted Ensemble (GOOWE), which assigns optimum weights to the component classifiers using a sliding window containing the most recent data instances.
