The ability to predict the career of students can be beneficial in a huge number of different
techniques which are connected with the education structure. Student's marks and the result …

The amount of students data in the education system databases is growing day by day, so
the knowledge taken out from these data need to be updated continuously. The training set …

Bagging, a widely used ensemble method, is simple and fast, and can generate
heterogeneous base classifiers. This research proposes an incremental learning algorithm …

The ability to predict the career of students can be beneficial in a huge number of different
techniques which are connected with the education structure. Student's marks in …

It is necessary to use Student dataset in order to analyze student's performance for future
improvements in study methods and overall curricular. Incremental learning methods are …

The ability to predict a student's performance could be useful in a great number of different
ways associated with university-level distance learning. Students' marks in a few written …

With data accumulating rapidly in education, it is possible for researchers to predict students
academic performance. Among the prediction models, the machine learning model is …

This paper investigates an interesting question of solving incremental learning problems
using ensemble algorithms. The motivation is to help classifiers learn additional information …

The incremental updating of classifiers implies that their internal parameter values can vary
according to incoming data. As a result, in order to achieve high performance, incremental …

An incremental learning algorithm based on weighted majority voting of an ensemble of
classifiers is introduced for supervised neural networks, where the voting weights are …