48 citations · 49 across the 4 of their papers we have counts for
4 papers
Characterizing instance hardness in classification and regression problems
Gustavo P. Torquette, Victor S. Nunes, Pedro Y. A. Paiva +2
Some recent pieces of work in the Machine Learning (ML) literature have demonstrated the usefulness of assessing which observations are hardest to have their label predicted accura…
Community-based anomaly detection using spectral graph filtering
Rodrigo Francisquini, Ana Carolina Lorena, Mariá C. V. Nascimento
Several applications have a community structure where the nodes of the same community share similar attributes. Anomaly or outlier detection in networks is a relevant and widely st…
PyHard: a novel tool for generating hardness embeddings to support data-centric analysis
Pedro Yuri Arbs Paiva, Kate Smith-Miles, Maria Gabriela Valeriano +1
For building successful Machine Learning (ML) systems, it is imperative to have high quality data and well tuned learning models. But how can one assess the quality of a given data…
How Complex is your classification problem? A survey on measuring classification complexity
Ana C. Lorena, Luís P. F. Garcia, Jens Lehmann +2
Characteristics extracted from the training datasets of classification problems have proven to be effective predictors in a number of meta-analyses. Among them, measures of classif…