Browsing by Author "Gomes, Heitor Murilo"

Now showing items 1-5 of 10

  • Adaptive random forests for evolving data stream classification

    Gomes, Heitor Murilo; Bifet, Albert; Read, Jesse; Barddal, Jean Paul; Enembreck, Fabrício; Pfahringer, Bernhard; Holmes, Geoffrey; Abdessalem, Talel (Springer, 2017)
    Random forests is currently one of the most used machine learning algorithms in the non-streaming (batch) setting. This preference is attributable to its high learning performance and low demands with respect to input ...
  • Boosting decision stumps for dynamic feature selection on data streams

    Barddal, Jean Paul; Enembreck, Fabrício; Gomes, Heitor Murilo; Bifet, Albert; Pfahringer, Bernhard (2019)
    Feature selection targets the identification of which features of a dataset are relevant to the learning task. It is also widely known and used to improve computation times, reduce computation requirements, and to decrease ...
  • CS-ARF: Compressed adaptive random forests for evolving data stream classification

    Bahri, Maroua; Gomes, Heitor Murilo; Bifet, Albert; Maniu, Silviu (IEEE, 2020)
    Ensemble-based methods are one of the most often used methods in the classification task that have been adapted to the stream setting because of their high learning performance achievement. For instance, Adaptive Random ...
  • 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 ...