Educational Data Classification using Data Mining and Kernel Ensemble Classifier
Sheo Kumar
Dr. Sheo Kumar, Professor, Department of Computer Science and Engineering, CMR Engineeringg College, Hyderabad (Telangana), India.
Manuscript received on 15 November 2019 | Revised Manuscript received on 23 December 2019 | Manuscript Published on 31 December 2019 | PP: 667-670 | Volume-9 Issue-2S4 December 2019 | Retrieval Number: B12381292S419/2019©BEIESP | DOI: 10.35940/ijitee.B1238.1292S419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The success of students gives the good name for institution and it become popular. Due to the large number of student’s database it is difficult to identify the performance and activities of each student. The educational data mining is used to identify the performance and status of the students individually. In this study, the Educational Data Classification (EDC) using data mining technique and kernel ensemble classification using Support Vector Machine (SVM) based kernels like linear, polynomial, quadratic and Radial Basis Function (RBF) is discussed. Initially the data preprocessing is made to remove the raw data into understandable format. The SVM kernels like linear, polynomial, quadratic and radial basis function based ensemble classifier is used for classification of student’s data. The data mining is used for making final decision of student’s performance in class like activities and interaction with electronic learning system. The performance of the system is evaluated by kalboard 360 database. The performance of the system is made by classification accuracy of 72.52% using SVM kernel ensemble classification.
Keywords: Educational Data Classification, Data Mining, SVM Kernels, Kalboard 360 Database.
Scope of the Article: Data Mining