Browsing by Author "Gomes, Heitor Murilo"

Now showing items 6-10 of 14

  • Inferring trust using personality aspects extracted from texts

    Granatyr, Jones; Gomes, Heitor Murilo; DIas, João Miguel; Paiva, Ana Maria; Nunes, Maria Augusta Silveira Netto; Scalabrin, Edson Emílio; Spak, Fábio (IEEE, 2019)
    Trust mechanisms are considered the logical protection of software systems, preventing malicious people from taking advantage or cheating others. Although these concepts are widely used, most applications in this field do ...
  • Mining attribute evolution rules in dynamic attributed graphs

    Fournier-Viger, Philippe; He, Ganghuan; Lin, Jerry Chun-Wei; Gomes, Heitor Murilo (Springer, 2020)
    A dynamic attributed graph is a graph that changes over time and where each vertex is described using multiple continuous attributes. Such graphs are found in numerous domains, e.g., social network analysis. Several studies ...
  • On dynamic feature weighting for feature drifting data streams

    Barddal, Jean Paul; Gomes, Heitor Murilo; Enembreck, Fabrício; Pfahringer, Bernhard; Bifet, Albert (Springer, 2016)
    The ubiquity of data streams has been encouraging the development of new incremental and adaptive learning algorithms. Data stream learners must be fast, memory-bounded, but mainly, tailored to adapt to possible changes ...
  • On ensemble techniques for data stream regression

    Gomes, Heitor Murilo; Montiel, Jacob; Mastelini, Saulo Martiello; Pfahringer, Bernhard; Bifet, Albert (IEEE, 2020)
    An ensemble of learners tends to exceed the predictive performance of individual learners. This approach has been explored for both batch and online learning. Ensembles methods applied to data stream classification were ...
  • Performance measures for evolving predictions under delayed labelling classification

    Grzenda, Maciej; Gomes, Heitor Murilo; Bifet, Albert (IEEE, 2020)
    For many streaming classification tasks, the ground truth labels become available with a non-negligible latency. Given this delayed labelling setting, after the instance data arrives and before its true label is known, the ...