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
D. M. Epi, Edificio Principal · 2014
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 distribu- tion. The possibilistic relevances are ordered with the help of a fuzzy ranking. A practical problem where the most informative software met- rics are searched for in an automatic grading problem is solved with this technique.