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 |
Pagination | 299–308 |
Publisher | Springer International Publishing |
Conference Location | Cham |
ISBN Number | 978-3-319-08644-6 |
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. |