Optimization of Classifiers Ensemble Construction: Case Study of Educational Data Mining

2019. Т. 19, No 4. С. 139–143 139 Introduction Educational Data Mining and Learning Analytics (EDM/LM) are promising scientific field to enhance of teaching and learning technologies of traditional and e-learning education [1–5] and to manage of various forms of constructivist education [6]. The wide availability of data mining tools such as R, scikit-learn for Pyton, and Weka [7] allows us to solve one of the main tasks of EDM/LA: to forecast of student's performance and to help the needy [8, 9]. Most commonly, this task is resolved by using of individual classifiers with learning following algorithms [10]: Naïve Bayes (NB), Decision Tree (J48), Multi-Layer Perceptron (MLP), Nearest Neighbors (1NN) and Support Vector Machine (SVM) and other algorithms from the top-10 list [11]. On the other hand, in pedagogical practice to identify problematic students and to solve their fate are used collective expert decisions. In this sense, in the EDM/LA we should use one of the metalearning approaches consists of “learning from base learners” [12, 13]. The general purpose of this paper is to compare capacity of two types of heterogenous ensembles. First type of ensemble was created by base classifiers used NB, J48, MLP, 1NN and SVM and attribute structure improved by Ranker Search method application. The second ensemble consists of five homogeneous ensembles created by AdaBoost.M1 procedure from each of five base classifiers.