The decision tree and neural network models are considered as one of the fastest and easy-to-use techniques having the ability to learn from classified data patterns. These models can be employed in detecting result anomalies measurable under normal circumstances on the bases that the student is healthy, had no problem and sat for exams. The existing techniques lack merit and integrity to efficiently detect irregularities found between student continuous assessments and exam scores. The addition of weights and calibrated values aided the learning process and addressed the problem facing the existing methods in operation. This provided an instance of having suitable control over the objective function in overcoming the identified problem. The added calibrated value helped control wrongly classified data patterns and improved the intelligence of the model. In this paper, the K-fold cross-validation test was employed to have a better classification report with the best split. This research was aimed to provide a comparative analysis of neural network and decision tree model for detecting result anomalies. The functionality of both models was used as a measure to check against result anomalies. This resulted in 96% and 91% accuracy with feed-forward multi-layered neural network and decision tree technique.
Cite this paper
Ziweritin, S. , Baridam, B. B. and Okengwu, U. A. (2022). A Comparative Analysis of Neural Network and Decision Tree Model for Detecting Result Anomalies. Open Access Library Journal, 9, e8549. doi: http://dx.doi.org/10.4236/oalib.1108549.
Hawkins, D.M. (2001) The Detection of Errors in Multivariate Data Using Principal Components. Journal of the American Statistical Association, 69, 340-344.
https://doi.org/10.1080/01621459.1974.10482950
Huang, G.B., Zhu, Q.Y. and Siew, C.K. (2016) Extreme Learning Machine: Theory and Applications. International Journal of Neurocomputing, 70, 489-501.
https://doi.org/10.1016/j.neucom.2005.12.126
Aderemi, O.A. and Andronicus, A.A. (2017) A Survey of Machine-Learning and Nature-Inspired Based Credit Card Fraud Detection Techniques. International Journal of System Assurance Engineering and Management, 8, 937-953.
https://doi.org/10.1007/s13198-016-0551-y
Talwar, A. and Kumar, Y. (2013) Machine Learning: An Artificial Intelligence Methodology. International Journal of Engineering and Computer Science, 2, 3400-3404.
Hamza, O.S., Ruqayyah, S. and Mohammed, O. (2016) Detecting Anomalies in Students, Results Using Decision Trees. International Journal of Modern Education and Computer Science (MECS), 8, 1312-1317. https://doi.org/10.5815/ijmecs.2016.07.04
Farid, D.M., Harbi, N. and Rahman, M.Z. (2010) Combining Naive Bayes and Decision Tree for Adaptive Intrusion Detection. International Journal of Network Security and Its Applications, 2, 12-25. https://doi.org/10.5121/ijnsa.2010.2202
Peddabachigari, S., Abraham, A. and Grosan, C. (2007) Modeling Intrusion Detection System Using Hybrid Intelligent Systems. Journal of Network and Computer Applications, 30, 114-132. https://doi.org/10.1016/j.jnca.2005.06.003
Jha, J. and Ragha, L. (2013) Intrusion Detection System Using Support Vector Machine. International Journal of Applied Information System (IJAIS), 5, 25-30.
Joseph, J.F., Das, A. and Seet, B.C. (2011) Cross-Layer Detection of Sinking Behavior in Wireless Ad Hoc Networks Using SVM and FDA. IEEE Transaction on Dependable and Secure Computing, 8, 12-23. https://doi.org/10.1109/TDSC.2009.48
Wiyono, S. and Abidin, T. (2019) Comparative Study of Machine Learning KNN, SVM, and Decision Tree Algorithm to Predict Student’s Performance. International Journal of Research, 7, 1-13. https://doi.org/10.29121/granthaalayah.v7.i1.2019.1048
Abdellatif, H., Mohamed, S. and Mohamed, F. (2016) Face Recognition: Synthesis of Classification Methods. International Journal of Computer Science and Information Security (IJCSIS), 14, 5-11.
Abhishek, K. and Anil, P. (2019) Implement of Students Result by Using Genetic Algorithm. International Journal of Computer Sciences and Engineering, 7, 51-56.
Anyanwu, M.N. and Shiva, S.G. (2009) Comparative Analysis of Serial Decision Tree Classification Algorithms. International Journal of Computer Science and Security (IJCSS), 3, 37-46.
Aneetha, A.S. and Bose, S. (2012) The Combined Approach for Anomaly Detection Using Neural Networks and Clustering Techniques. Computer Science and Engineering: An International Journal (CSEIJ), 2, 37-46.
https://doi.org/10.5121/cseij.2012.2404
Badr, H.H.E., Abdelkarim, M. and Mohammed, E. (2014) A Comparative Study of Decision Tree ID3 and C4.5. International Journal of Advanced Computer Science and Applications, 4, 13-19. https://doi.org/10.14569/SpecialIssue.2014.040203
Ziweritin, S., Baridam, B.B. and Okengwu, U.A. (2020) Neural Network Model for Detection of Result Anomalies in Higher Education. Scientia Africana: An International Journal of Pure and Applied Sciences (IJPAS), 19, 91-104.
John, E.B., Derek, T.A. and Chee, S.C. (2017) Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools, and Challenges for the Community. Journal of Applied Remote Sensing, 11, 609. https://doi.org/10.1117/1.JRS.11.042609
Omlin, C.W. and Snyders, S. (2003) Inductive Bias Strength in Knowledge-Based Neural Networks: Application to Magnetic Resonance Spectroscopy of Breast Tissues. Artificial Intelligence in Medicine, 28, 30-54.
https://doi.org/10.1016/S0933-3657(03)00062-9
Yedjour, D., Yedjour, H. and Benyettou, A. (2011) Explaining Results of Artificial Neural Networks. Journal of Applied Sciences, 2, 1-45.
https://doi.org/10.3923/jas.2011.2855.2860
Balogun, A.O., Mabayoje, M.A., Salihu, S. and Arinze, S.A. (2015) Enhanced Classification via Clustering Techniques Using Decision Tree for Feature Selection. International Journal of applied Information System (IJAIS), 9, 11-16.
https://doi.org/10.5120/ijais2015451425
Bhargava, N., Sharma, G., Bhargava, R. and Mathuria, M. (2013) Decision Tree Analy- sis on j48 Algorithm for Data Mining. Proceedings of International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE), 2, 45-98.
Patel, H.P. and Prajapati, P. (2018) Study and Analysis Tree Based Classification Algorithms. International Journal of Computer Sciences and Engineering (IJCSE), 6, 74-78.
https://doi.org/10.26438/ijcse/v6i10.7478
Soranamageswari, M. and Meena, C. (2020) Histogram Based Image Spam Detection Using Back Propagation Neural Networks. Global Journal of Computer Science and Te- chnology (GJCST), 9, 62-67.
Lakshmi, T.M., Martin, A., Begum, R.M. and Venkatesan, V.P. (2013) An Analysis on Performance of Decision Tree Algorithms Using Students Qualitative Data. International Journal of Modern Education and Computer Science (IJMECS), 5, 18-27.
https://doi.org/10.5815/ijmecs.2013.05.03
Li, Y., Xing, H., Hua, Q. and Wang, X. (2014) Classification of BGP Anomalies Using Decision Trees and Fuzzy Rough Sets. IEEE International Conference on Systems, Man and Cybernetics (SMC), San Diego, 5-8 October 2014, 1312-1317.
https://doi.org/10.1109/SMC.2014.6974096
Chien-Liang, L. and Ching-Lung, F. (2019) Evaluation of CART, CHAID, and QUEST Algorithms: A Case Study of Construction Defects in Taiwan. Journal of Asian Architecture and Building Engineering, 18, 1-40.
https://doi.org/10.1080/13467581.2019.1696203
Zhang, X. and Jiang, S.A. (2012) A Splitting Criteria Based on Similarity in Decision Tree Learning. JSW, 7, 82-1775. https://doi.org/10.4304/jsw.7.8.1775-1782
Huang, M. and Hsu, Y. (2012) Fetal Distress Prediction Using Discriminant Analysis, Decision Tree, and Artificial Neural Network. Journal of Biomedical Science and Engineering (JBSE), 5, 526-533. https://doi.org/10.4236/jbise.2012.59065
