Received: December 1, 2021. Revised: January 17, 2022. 316 International Journal of Intelligent Engineering and Systems, Vol.15, No.2, 2022 DOI: 10.22266/ijies2022.0430.29 Genetically Optimized Ensemble Classifiers for Multiclass Student Performance Prediction Safira Begum 1 * Sunita S Padmannavar 2 1 Visvesvarya Technological University – RRC, India 2 Gogte Institute of Technology, India * Corresponding author’s Email: safirabgm@gmail.com Abstract: The knowledge obtained from data can be useful for the improvement of education systems, giving rise to a research space called Educational Data Mining (EDM). EDM covers the development of methods to explore information collected from educational environments, allowing to understand students more effectively and adequately, providing better educational benefits to them. Machine learning (ML) technologies are growing considerably in recent years. The field of data mining in education provides researchers and educators with metrics of success, failure, dropout, and more, allowing students to guess. The main reason for dropping out of school is not studying. Several researchers have proposed various educational data mining techniques to predict student performance and analyzed the techniques found in educational datasets. This paper proposes a student predictive model with the use of ensemble classifiers. Initially data is pre-processed and an analysis of the correlation between the entrance attributes was carried out to identify the existence of possible redundancies between them, resulting from a very high positive correlation. The filtered attribute is trained and tested with Boosting, Bagging and Random subspace classifiers. Further to improve the accuracy of predictive model genetic algorithm is applied on three classifiers. Genetic Algorithm is an approach used to find optimized solution to search problems and it intend to increase the probability of solving the problem. The process of optimization involves selection of the best option from the available set of options to achieve the desired goal. Selection is done such that the efficiency can be maximized and error can be minimized. An analysis of the correlation between the entrance attributes was carried out to identify the existence of possible redundancies between them, resulting from a very high positive correlation. There is significant improvement in classifier accuracy, when tested mathematic and Portuguese data i.e. 3 % and 11% respectively. Keywords: Machine learning, Educational data mining, Boosting, Bagging, Random subspace. 1. Introduction Educational Data Mining (EDM) deals with educational data in Data mining. It is a research area that has emerged in recent years and is used by researchers in different areas, such as education, computer science, intelligent tutoring systems, statistics and psychology in the analysis of large data sets to solve educational research problems [1]. EDM is concerned with the development of methods to explore information collected from educational environments, allowing the understanding of students more effectively and adequately, providing better educational benefits to them [2]. This research deals with the use of Machine Learning, as a tool to help school managers in checking and combating school dropout. From the analysis of historical data stored in databases, the aim was to build analytical models that can learn from data, identify patterns and help in decision making. Following are the major steps involve in data mining (DM). Step I - Pre-processing: Data obtained from educational systems must first be pre-processed and transformed into a format suitable for data mining. The major tasks in this step includes: clean-up, attribute selection, and data integration. Step II - Data Mining: This is the main phase and in this context, data mining techniques are applied to