|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|
|Publication Type||Conference Paper|
|Year of Publication||2014|
|Authors||Otero, José, Suárez Maria Del Rosari, Palacios Ana, Couso Inés, and Sánchez Luciano|
|Editor||Cornelis, Chris, Kryszkiewicz Marzena, Ślȩzak Dominik, Ruiz Ernestina Menasalvas, Bello Rafael, and Shang Lin|
|Conference Name||Rough Sets and Current Trends in Computing|
|Publisher||Springer International Publishing|
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.