173 citations · 232 across the 7 of their papers we have counts for
7 papers
Experiential-Informed Data Reconstruction for Fishery Sustainability and Policies in the Azores
Brenda Nogueira, Gui M. Menezes, Nuno Moniz +1
Fishery analysis is critical in maintaining the long-term sustainability of species and the livelihoods of millions of people who depend on fishing for food and income. The fishing…
Explainable Predictive Maintenance
Sepideh Pashami, Slawomir Nowaczyk, Yuantao Fan +13
Explainable Artificial Intelligence (XAI) fills the role of a critical interface fostering interactions between sophisticated intelligent systems and diverse individuals, including…
Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation
Sérgio Jesus, José Pombal, Duarte Alves +5
Evaluating new techniques on realistic datasets plays a crucial role in the development of ML research and its broader adoption by practitioners. In recent years, there has been a…
A Benchmark dataset for predictive maintenance
Bruno Veloso, João Gama, Rita P. Ribeiro +1
The paper describes the MetroPT data set, an outcome of a eXplainable Predictive Maintenance (XPM) project with an urban metro public transportation service in Porto, Portugal. The…
Model Optimization in Imbalanced Regression
Aníbal Silva, Rita P. Ribeiro, Nuno Moniz
Imbalanced domain learning aims to produce accurate models in predicting instances that, though underrepresented, are of utmost importance for the domain. Research in this field ha…
UBL: an R package for Utility-based Learning
Paula Branco, Rita P. Ribeiro, Luis Torgo
This document describes the R package UBL that allows the use of several methods for handling utility-based learning problems. Classification and regression problems that assume no…
A Survey of Predictive Modelling under Imbalanced Distributions
Paula Branco, Luis Torgo, Rita Ribeiro
Many real world data mining applications involve obtaining predictive models using data sets with strongly imbalanced distributions of the target variable. Frequently, the least co…