Prototype selection to improve monotonic nearest neighbor
Published in Engineering Applications of Artificial Intelligence, 2017
Recommended citation: José-Ramón Cano, Naif Aljohani, Rabeeh Abbasi, Jalal Alowidbi, Salvador García, "Prototype selection to improve monotonic nearest neighbor." Engineering Applications of Artificial Intelligence, 2017. http://www.sciencedirect.com/science/article/pii/S0952197617300295
Student surveys occupy a central place in the evaluation of courses at teaching institutions. At the end of each course, students are requested to evaluate various aspects such as activities, methodology, coordination or resources used. In addition, a final qualification is given to summarize the quality of the course. The prediction of this final qualification can be accomplished by using monotonic classification techniques. The outcome offered by these surveys is particularly significant for faculty and teaching staff associated with the course.