A combinational incremental ensemble of classifiers as a technique for predicting students’ performance in distance education

Application of Data Mining and Process Mining approaches for improving e-Learning Processes

“… Monitoring the progress of learning to detect in real time the undesirable behavior of students, such as the termination of training, low motivation, incorrect use of educational forums, abuse, fraud, etc., creating warnings to the parties concerned [13], provision feedback to the teachers in order to support decision-making on the improvement of student learning, the adoption of pre-emptive actions to remedy the situation [10];…”

Review of Medical Disease Symptoms Prediction Using Data Mining Technique

“…S. D. Kotal and S. K. Roy Bhowmik Et al. [13] The strategy is produced utilizing various straight relapse procedure with five part models, to be specific ECMWF, [14] This paper means to fill the hole between experimental expectation of understudy execution and the current ML methods in a separation instruction condition. The examined method, expects to explore wonders in instructive technique from the purpose of causal understanding perspective.…”

Incremental Feature Subsetting useful for Big Feature Space Problems

“…Recent literature [32,34,35,37,38] contains numerous references to the use of hybrid feature selection algorithms. Based on a filter ranking, these algorithms perform an incremental wrapper selection over feature ranking [29].…”

“…To combine and/or merge the features optimally, many researchers have worked on several feature space partitioning methods which often involve clustering or classification of features [4,6]. Research has shown that the simultaneous partitioning of the feature space and an assignment of a combined classifier to each of the subsets yields optimal feature space [37]. Given a set of n features, it can be partitioned into batches of features (see Figure 4) and each batch can inturn be worked upon to reduce the complexity of computation.…”