Investigating the Robustness of Text Mining Classification Algorithms: A Study of Algorithm and Expert Performance on Class-Inconsistent Data

Hüseyin Ataseven; Ömay Çokluk-bökeoglu

doi:10.35377/saucis...1626239

EN

Investigating the Robustness of Text Mining Classification Algorithms: A Study of Algorithm and Expert Performance on Class-Inconsistent Data

Abstract

This study compares the classification accuracy of text mining algorithms for foreign language proficiency exam items. The dataset included 2,868 items from ÜDS English tests (2006–2012) across Natural and Applied Sciences (n=956), Health Sciences (n=956), and Social Sciences (n=956). Algorithms tested were k-Nearest Neighbors (kNN), Naïve Bayes (NB), Naïve Bayes-Kernel (NB-K), Random Forest (RF), and Support Vector Machines (SVM). Binary classification accuracies ranged from 83.08% (NB) to 92.48% (SVM), while multiclass accuracies ranged from 71.93% (NB) to 84.96% (kNN). Expert analysis and cross-validation identified class-inconsistent items that negatively affected accuracy. Removing these items improved binary classification by 7.39%–9.83% and multiclass classification by 10.58%–17.89%. Among algorithms, kNN was least impacted by class-inconsistent data. These findings highlight the importance of addressing inconsistencies for improving algorithmic performance, with kNN showing robust results across scenarios.

Keywords

References

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Details

Primary Language

English

Subjects

Software Engineering (Other)

Journal Section

Research Article

Authors

Hüseyin Ataseven ^*
0000-0001-9992-4518
Türkiye

Ömay Çokluk-bökeoglu
0000-0002-3879-9204
Türkiye

Early Pub Date

September 24, 2025

Publication Date

September 30, 2025

Submission Date

January 24, 2025

Acceptance Date

July 16, 2025

Published in Issue

Year 2025 Volume: 8 Number: 3

DOI

https://doi.org/10.35377/saucis...1626239

IZ

https://izlik.org/JA26BH75JH

Cite

RIS / Bibtex

APA

Ataseven, H., & Çokluk-bökeoglu, Ö. (2025). Investigating the Robustness of Text Mining Classification Algorithms: A Study of Algorithm and Expert Performance on Class-Inconsistent Data. Sakarya University Journal of Computer and Information Sciences, 8(3), 422-440. https://doi.org/10.35377/saucis...1626239

AMA

1.Ataseven H, Çokluk-bökeoglu Ö. Investigating the Robustness of Text Mining Classification Algorithms: A Study of Algorithm and Expert Performance on Class-Inconsistent Data. SAUCIS. 2025;8(3):422-440. doi:10.35377/saucis.1626239

Chicago

Ataseven, Hüseyin, and Ömay Çokluk-bökeoglu. 2025. “Investigating the Robustness of Text Mining Classification Algorithms: A Study of Algorithm and Expert Performance on Class-Inconsistent Data”. Sakarya University Journal of Computer and Information Sciences 8 (3): 422-40. https://doi.org/10.35377/saucis. 1626239.

EndNote

Ataseven H, Çokluk-bökeoglu Ö (September 1, 2025) Investigating the Robustness of Text Mining Classification Algorithms: A Study of Algorithm and Expert Performance on Class-Inconsistent Data. Sakarya University Journal of Computer and Information Sciences 8 3 422–440.

IEEE

[1]H. Ataseven and Ö. Çokluk-bökeoglu, “Investigating the Robustness of Text Mining Classification Algorithms: A Study of Algorithm and Expert Performance on Class-Inconsistent Data”, SAUCIS, vol. 8, no. 3, pp. 422–440, Sept. 2025, doi: 10.35377/saucis...1626239.

ISNAD

Ataseven, Hüseyin - Çokluk-bökeoglu, Ömay. “Investigating the Robustness of Text Mining Classification Algorithms: A Study of Algorithm and Expert Performance on Class-Inconsistent Data”. Sakarya University Journal of Computer and Information Sciences 8/3 (September 1, 2025): 422-440. https://doi.org/10.35377/saucis. 1626239.

JAMA

1.Ataseven H, Çokluk-bökeoglu Ö. Investigating the Robustness of Text Mining Classification Algorithms: A Study of Algorithm and Expert Performance on Class-Inconsistent Data. SAUCIS. 2025;8:422–440.

MLA

Ataseven, Hüseyin, and Ömay Çokluk-bökeoglu. “Investigating the Robustness of Text Mining Classification Algorithms: A Study of Algorithm and Expert Performance on Class-Inconsistent Data”. Sakarya University Journal of Computer and Information Sciences, vol. 8, no. 3, Sept. 2025, pp. 422-40, doi:10.35377/saucis. 1626239.

Vancouver

1.Hüseyin Ataseven, Ömay Çokluk-bökeoglu. Investigating the Robustness of Text Mining Classification Algorithms: A Study of Algorithm and Expert Performance on Class-Inconsistent Data. SAUCIS. 2025 Sep. 1;8(3):422-40. doi:10.35377/saucis. 1626239