Volume & Issue no: Volume 11, Issue 6, November - December 2022
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Title: |
Random Forest Based Classifiers for Detecting Result Anomalies |
Author Name: |
Stanley Ziweritin, Iduma Aka Ibiam |
Abstract: |
Random forest(RF) is a supervised machine learning
approach that experts use to build and integrate many decision
trees into a single forest. It takes considerable expertise to detect
result anomalies depending on the degree of disparity between
students CA and exam scores. It is doable to train RF-based
classifiers to accurately identify anomalies with imbalanced data
categorization. The aim is to develop RF-based classifiers
capable of detecting abnormalities in student results, such as
when a student performed remarkably well on the exam but
poorly on the CA, or vice versa. The SMOTE technique was
used to resolve unbalanced data categorization, which helped
reduce dataset bias toward the majority class while also ensuring
that the minority class received an acceptable sample size.
Strong decision-makers were grouped into a class of majority
vote using the grid search and randomized function. Trees
capacity to learn from small data samples was arbitrarily
constrained by the uniformly distribution function, which
increased model accuracy and reduced tree correlation.
Comparatively, the Classification, Adaboost, and
GradientBoosting classifiers produced accuracy scores of
99.00%, 95.17%, and 81.50% respectively |
Cite this article: |
Stanley Ziweritin, Iduma Aka Ibiam , "
Random Forest Based Classifiers for Detecting Result Anomalies" , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) ,
Volume 11, Issue 6, November - December 2022 , pp.
009-018 , ISSN 2278-6856.
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