Now showing items 1-2 of 2

    • Learning to classify software defects from crowds: a novel approach 

      Hernández-González J.; Rodríguez D.; Inza I.; Rachel H.; Lozano J.A. (Applied Soft Computing, 2017-11-01)
      In software engineering, associating each reported defect with a cate- gory allows, among many other things, for the appropriate allocation of resources. Although this classification task can be automated using stan- dard ...
    • A note on the behavior of majority voting in multi-class domains with biased annotators 

      Hernández-González J.; Inza I.; Lozano J.A. (IEEE Transactions on Knowledge and Data Engineering, 2018-05)
      Majority voting is a popular and robust strategy to aggregate different opinions in learning from crowds, where each worker labels examples ac- cording to their own criteria. Although it has been extensively studied in the ...