An Ensemble-Based Decision Tree Approach for Educational Data Mining

Abstract

Nowadays, data mining and machine learning techniques are applied to a variety of different topics (e. g., healthcare and disease, security, decision support, sentiment analysis, education, etc.). Educational data mining investigates the performance of students and gives solutions to enhance the quality of education. The aim of this study is to use different data mining and machine learning algorithms on actual data sets related to students. To this end, we apply two decision tree methods. The methods can create several simple and understandable rules . Moreover, the performance of a decision tree is optimized by using an ensemble technique named Rotation Forest algorithm. Our findings indicate that the Rotation Forest algorithm can enhance the performance of decision trees in terms of different metrics. In addition, we found that the size of tree generated by decision trees ensemble were bigger than simple ones. This means that the proposed methodology can reveal more information concerning simple rules.

Integration of Ensemble and Evolutionary Machine Learning Algorithms for Monitoring Diver Behavior Using Physiological Signals

“…Exploitation depends on the local search capability in the promising regions of design space found in the exploration phase. One of the challenging task in the estimation of EOAs is to find a proper balance between exploration and exploitation [18]. There are many applications of EOAs in different industries, such as PID controller in DC motors.…”

A comparative study of ensemble methods in the field of education: Bagging and Boosting algorithms

“…It is worth noting that the researchers who conduct studies on data mining and machine learning have fallen behind in discovering the success of Ensemble-based learning methods in terms of classification and prediction-based decisionmaking (Polikar, 2012). Nevertheless, with the studies carried out in recent years, it has been seen that a great deal of knowledge and literature have been obtained especially in the field of education (Abdar et al, 2018;Abellán & Castellano, 2017;Aggarwal et al, 2021;Almasri et al, 2019;Ashraf et al, 2021;Ashraf et al, 2020;Arun et al, 2021;Guo et al, 2021;Karalar et al, 2021;Keser & Aghalarova, 2022;Kotsiantis et al, 2010;Injadat et al, 2020a;Injadat et al, 2020b;Premalatha & Sujatha, 2021). This comparative study focusing on Bagging and Boosting (Akman, 2010;Zhou, 2012) algorithms that are the most well-known Ensemble methods may contribute to the literature and, particularly the field of educational data mining, in order to list and utilize the concept of Ensemble Learning and its methods among advanced statistical methods in the field of education.…”

A Case Study of Predicting Banking Customers Behaviour by Using Data Mining

“…Data mining is defined as a process that uses mathematical, statistical, artificial intelligence and machine learning techniques to extract and identify useful information and subsequently gain knowledge from databases. Data mining algorithms have been widely used in range of research fields such as healthcare and medicine [5][6][7], sentiment analysis [8,9], education [10] etc. The purpose of applying data mining in bank industry is to use the available data to retain its best customers and to identify opportunities sell them additional services.…”