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

TitleSelecting 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 TypeConference Paper
Year of Publication2014
AuthorsOtero, José, Suárez Maria Del Rosari, Palacios Ana, Couso Inés, and Sánchez Luciano
EditorCornelis, Chris, Kryszkiewicz Marzena, Ślȩzak Dominik, Ruiz Ernestina Menasalvas, Bello Rafael, and Shang Lin
Conference NameRough Sets and Current Trends in Computing
Pagination299–308
PublisherSpringer International Publishing
Conference LocationCham
ISBN Number978-3-319-08644-6
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.