Abstract
In the present state of affairs, the motive behind every educational organization is to uplift the academic achievement of students. Educational data mining (EDM) is an upward field of research, and it is very helpful for academic institutions to predict the academic performance of the students. Educational datasets are the basis of various predictive models. Quality of these models can be improved by using feature selection (FS). To get the required benefits from the available data, there must be some tools for analysis and prediction. In lieu of the above, machine learning/data mining are most suitable. In educational data mining, for better accuracy of prediction models’ and quality of various educational datasets, feature selection (FS) plays a vital role. Feature selection (FS) algorithms abolish inappropriate information from the repositories of educational background so that performance of classifier in terms of accuracy could be increased and the same could be used for better decision. In lieu of the above, a best feature selection algorithm must be selected. In this paper, two filter selection approaches namely correlation feature selection (CFS) and wrapper-based feature selection have been used to demonstrate the importance of selection of a feature subset for a classification problem. The present paper aims to find the detailed investigation of filter feature selection algorithms along with the classification algorithms on a given dataset. We found result with numerous numbers of features from various Feature selection algorithms and classifiers which will help the researcher to discover the most excellent mixture of filter feature selection algorithms and its associated classifiers. The result indicates that SMO and J48 have the highest accuracy measures with the correlation feature selection algorithms, while Naïve Bayes has the highest accuracy measures with the wrapper subset feature selection algorithms for predicting high, medium and low grade for the students.
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References
C. Jalota, R. Agrawal, Analysis of data mining using classification, in IEEE International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCON) (2019)
P. Kavipriya, A. Karthikeyan, Hybridization of enhanced ant colony optimization (ACO) and genetic algorithm (GA) for feature extraction in educational data mining. J. Adv. Res. Dyn. Control Syst. 10, 1278–1284 (2018)
S. Hussain, N.A. Dahan, F.M. By-Law, N. Ribata, Educational data mining and analysis of students’ academic performance using WEKA. Indonesian J. Electr. Eng. Comput. Sci. 9 (2018)
M. Zaffar, M.A. Hashmani, K.S. Savita, Performance analysis of feature selection algorithm for educational data mining, in IEEE Conference on Big Data and Analytics (ICBDA) (2017)
P. Kavipriya, K. Karthikeyan, A comparative study of feature selection algorithms in data mining. Int. J. Adv. Res. Comput. Commun. Eng. 6(11) (2017) ISO 3297
P. Kavipriya, K. Karthikeyan, Case study: on improving student performance prediction in education systems using enhanced data mining techniques. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 7(5) (2017)
M. Zaffar, M.A. Hashmani, K.S. Savita, Performance analysis of feature selection algorithm for educational data mining, in Big Data and Analytics (ICBDA) (2017), pp. 7–12
B. Mueen, A. Zafar, U. Manzoor, Modeling and predicting students’ academic performance using data mining technique. Int. J. Modern Educ. Comput. Sci. 8, 36 (2016)
A. Figueira, Predicting grades by principal component analysis: a data mining approach to learning analysis, in IEEE 16th International Conference in Advanced Learning Technologies (ICALT) (2016), pp. 465–467
S. Sivakumar, S. Venkataraman, R. Selvara, Predictive modeling of student dropout indicators in educational data mining using improved decision tree. Indian J. Sci. Technol. 9 (2016)
Veerabhadrappa, L. Rangarajan, Multi-level dimensionality reduction methods using feature selection and feature extraction. Int. J. Artif. Intell. Appl. 1, 54–68 (2010)
H.B. Sandya, P. Hemanth Kumar, S.K.R. Himanshi Bhudiraja, Fuzzy rule based feature extraction and classification. Int. J. Soft Comput. Eng. 3(2), 42–47 (2013)
T. Kajdanowic, P. Kazienko, P. Doskocz, in Label-Dependent Feature Extraction in Social Networks for Node Classification. Lecture notes in computer science, vol. 6430 (Springer, 2010), pp. 89–102
V.P. Gladis, P. Rathi, P.S. Palani, A novel approach for feature extraction and selection on MRI images for brain tumor classification. Int. J. Comput. Sci. Inf. Technol. 2(1), 225–234 (2012)
C. Jalota, M. Munjal, Use of K means with feature extraction in content based image retrieval system, in 4th IEEE International Conference on Computing for Sustainable Global Development, pp. 6763–6768 (2017)
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Jalota, C., Agrawal, R. (2021). Feature Selection Algorithms and Student Academic Performance: A Study. In: Gupta, D., Khanna, A., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1165. Springer, Singapore. https://doi.org/10.1007/978-981-15-5113-0_23
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