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Paper Title : Identification of Classification Anomalies in Students’ Areas of Specialization using Ensemble Classifiers

ISSN : 2394-2231
Year of Publication : 2021

10.29126/23942231/IJCT-v8i4p3
Authors: Anthony Irungu Njina, Mary Asunta Gaceri

MLA Style: Anthony Irungu Njina, Mary Asunta Gaceri " Identification of Classification Anomalies in Students’ Areas of Specialization using Ensemble Classifiers " Volume 8 - Issue 4 July-August , 2021 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org

APA Style: Anthony Irungu Njina, Mary Asunta Gaceri " Identification of Classification Anomalies in Students’ Areas of Specialization using Ensemble Classifiers " Volume 8 - Issue 4 July-August , 2021 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org

Abstract
In this paper, we propose an ensemble of different classifiers and examine the distribution of anomalies in the classification reports of individual model results. The ensemble is constructed using three base classifiers: Multinomial Naïve Bayes (MNB), Support Vector Machines (SVM), and Random Forest (RF). We expect improved accuracy as a result of the combined prediction power of different algorithms, and hypothesize that a concurrence of high error rates within a class is an indication of classification anomaly. Results showed an improved accuracy for classes where individual F1 scores were within the range of the average F1 scores. However, we could not make similar observations for classes with low support, and in the classes identified with possible instances of misclassification. The results obtained from this experiment suggest that an ensemble model with data preprocessing is a more accurate model for predicting students’ subject area combinations

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Keywords
—— Misclassification, Anomaly Detection, Ensemble Learning, Students Tracking

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