Predicting Intentions of Students for Master Programs using a Chaos-induced Sine Cosine-based Fuzzy K-Nearest Neighbor Classifier

One of the essential tasks for the planning and development of talents training programs in different colleges of universities is to find how we can reasonably guide students to pursue a master's degree concerning their comprehensive situations. The purpose of this study is to develop a modified fuzzy k-Nearest Neighbor (FKNN) framework to predict the college students' intentions for master programs in advance, that is, students choose to attend the postgraduate exam or find a job after graduation. The proposed integrated framework combines the random forest (RF), FKNN, and a new chaos-enhanced sine cosine-inspired algorithm (CESCA). In this model, RF is employed to evaluate the importance of features in the dataset, while the FKNN is utilized to establish the relationship framework between the features and the college students' decisions to earn a master's degree or not. The proposed CESCA is utilized to tune the key parameters of the FKNN automatically. All eight variants of SCA have been rigorously compared based on 13 benchmark problems to validate the effectiveness of the proposed CESCA. Then, the CESCA-based FKNN (CESCA-FKNN) has been further compared against the other three classical classifiers in terms of four common performance metrics. The experimental results indicate that the proposed CESCA-FKNN can obtain the best classification accuracy. The results indicate that the established adaptive FKNN framework can be served as a powerful tool for college students' intention before pursuing a master's degree.