Imbalanced Learning Techniques for Improving the Performance of Statistical Models in Automated Essay Scoring

The problem of class imbalance occurs in many application domains, as in the case of essays. The imbalance of the number or samples among the classes presents a problem for predictive algorithms, both for classification or for regression. In this paper, we present imbalanced learning techniques for...

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Bibliographic Details
Published inProcedia computer science Vol. 159; pp. 764 - 773
Main Authors Filho, Aluizio Haendchen, Concatto, Fernando, Nau, Jonathan, Prado, Hércules A. do, Imhof, Diego Oscar, Ferneda, Edilson
Format Journal Article
LanguageEnglish
Published Elsevier B.V 2019
Subjects
Automated essay grading
Classification Algorithms
Imbalanced Learning
Machine learning
Classification Algorithms
Imbalanced Learning
Automated essay grading
Machine learning
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Summary:The problem of class imbalance occurs in many application domains, as in the case of essays. The imbalance of the number or samples among the classes presents a problem for predictive algorithms, both for classification or for regression. In this paper, we present imbalanced learning techniques for improving the performance of statistical models in automated essay grading. We analyze the performance of different types of statistical algorithms using machine learning and classification techniques, combined with different balancing methods. The results indicated a significant improvement of accuracy and average error when the algorithms were applied in a balanced corpus, comparing with the unbalanced ones.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2019.09.235