… learning-based classification models through the feature selection method and to … classification
algorithm was based on an online ensemble of classifiers that integrates an incremental …

… 2013 the increment in drop-out for students has escalated … The algorithms to be used in
this study for classification are … in this ensemble method which includes decision tree (DT), K-…

… than each of the individual classifiers on their own. The use of ensemble ML techniques for
this study will provide an improved solution to the problem of predicting SAR. To this end, this …

… have shown a positive increment from 2006 to 2017. In 2006, … of unemployment of
undergraduate students in Malaysia is … only focuses on undergraduate students. Data from other …

… technique named the combinational incremental ensemble of classifiers for student performance
prediction… In the proposed technique, three classifiers are combined where each of the …

… predictive performance as compared to individual classifiers, and … for the predictive model,
followed by students’ culture and … gives a prediction model in the form of an ensemble of weak …

… Classification is the data mining method used in this study by comparing the performance of
multiple classifiers including four decision … of ensemble model accuracy in predicting student …

… a classification approach in finding an enhanced evaluation method for students and predict
… most recent two decades, we have seen an exponential increment of data fellow, stock, and …

… classification techniques are also being used for prediction and analyses. Algorithm such as
decision … of data in decision trees which is solved by pruning and ensemble learning. The …

… the use of a method called the combinational incremental ensemble of classifiers to forecast
student … In this study, the regression and classification are performed to forecast the student …