52 citations · 55 across the 9 of their papers we have counts for
5 papers · 1 filter
Instance Hardness-Based Relevance for Imbalanced Regression
Vitor M. Leitao, Juscimara G. Avelino, George D. C. Cavalcanti +1
Imbalanced regression problems arise when the target variable has an asymmetric distribution, resulting in underrepresented value ranges in the dataset. Traditional approaches for…
DRES: Fake news detection by dynamic representation and ensemble selection
Faramarz Farhangian, Leandro A. Ensina, George D. C. Cavalcanti +1
The rapid spread of information via social media has made text-based fake news detection critically important due to its societal impact. This paper presents a novel detection meth…
PIPES: A Meta-dataset of Machine Learning Pipelines
Cynthia Moreira Maia, Lucas B. V. de Amorim, George D. C. Cavalcanti +1
Solutions to the Algorithm Selection Problem (ASP) in machine learning face the challenge of high computational costs associated with evaluating various algorithms' performances on…
Resampling strategies for imbalanced regression: a survey and empirical analysis
Juscimara G. Avelino, George D. C. Cavalcanti, Rafael M. O. Cruz
Imbalanced problems can arise in different real-world situations, and to address this, certain strategies in the form of resampling or balancing algorithms are proposed. This issue…
Imbalanced Regression Pipeline Recommendation
Juscimara G. Avelino, George D. C. Cavalcanti, Rafael M. O. Cruz
Imbalanced problems are prevalent in various real-world scenarios and are extensively explored in classification tasks. However, they also present challenges for regression tasks d…