@inproceedings{544,
  author = {José Otero and Maria Suárez and Ana Palacios and Inés Couso and Luciano Sánchez},
  editor = {Chris Cornelis and Marzena Kryszkiewicz and Dominik Ślȩzak and Ernestina Ruiz and Rafael Bello and Lin Shang},
  title = {Selecting the Most Informative Inputs in Modelling Problems with Vague Data Applied to the Search of Informative Code Metrics for Continuous Assessment in Computer Science Online Courses},
  abstract = {Sorting a set of inputs for relevance in modeling problems may be ambiguous if the data is vague. A general extension procedure is proposed in this paper that allows applying different deterministic or random feature selection algorithms to fuzzy data. This extension is based on a model of the relevance of a feature as a possibility distribution. The possibilistic relevances are ordered with the help of a fuzzy ranking. A practical problem where the most informative software metrics are searched for in an automatic grading problem is solved with this technique.},
  year = {2014},
  pages = {299-308},
  publisher = {Springer International Publishing},
  address = {Cham},
  isbn = {978-3-319-08644-6},
}