COMPARATIVE ANALYSIS OF CLASSIFIERS FOR EDUCATION CASE STUDY
Nurshahirah Abdul Malik*1, Mohd Shahizan Othman2 and Lizawati Mi Yusuf3
1, 2, 3 Faculty of Computing, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia.
Recently, classification is becoming a very valuable tool where a large amount of data is used on a wide range of decisions for the education sector. Classification is a method that used to group data based on predetermined characteristics. It is utilized to classify the item as indicated by the features for the predefined set of classes. The main significance of classification is to classify data from large datasets to find patterns out of it. Nevertheless, it is very important to choose the best classification algorithm which is also called as the classifier. Therefore, this research aims to conduct comparative evaluation between four classifiers which are Deep Neural Network (DNN), Random Forest (RF), Support Vector Machine (SVM) and Decision Tree (DT). All these classifiers have its own efficiency and have an important role in identifying the set of populations based on the training datasets. To choose the best classifiers among the four classifiers, the classifiers performance is required to be evaluated based on the performance metrics. The performance metrics of these classifiers were determined using accuracy and sensitivity rates. This study used education case study on student’s performance data for two subjects, Mathematics and Portuguese from two Portugal secondary schools and data on the student's knowledge of Electrical DC Machines subject. After comparing the accuracy and sensitivity rates, DNN has the highest accuracy and sensitivity rate of classification and can be used to further the education-based research in future.
Classifiers; Student Performance; Deep Neural Network; Support Vector Machine. FULL PAPER