Open Access Library Journal

Volume 9, Issue 3 (March 2022)

ISSN Print: 2333-9705   ISSN Online: 2333-9721

Google-based Impact Factor: 1.18  Citations  

A Comparative Analysis of Neural Network and Decision Tree Model for Detecting Result Anomalies

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DOI: 10.4236/oalib.1108549    240 Downloads   2,912 Views  Citations

ABSTRACT

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.

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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, 1-15. doi: 10.4236/oalib.1108549.

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