TY  - JOUR
T1  - Investigating the Robustness of Text Mining Classification Algorithms: A Study of Algorithm and Expert Performance on Class-Inconsistent Data
AU  - Ataseven, Hüseyin
AU  - Çokluk-bökeoglu, Ömay
PY  - 2025
DA  - September
Y2  - 2025
DO  - 10.35377/saucis...1626239
JF  - Sakarya University Journal of Computer and Information Sciences
JO  - SAUCIS
PB  - Sakarya University
WT  - DergiPark
SN  - 2636-8129
SP  - 422
EP  - 440
VL  - 8
IS  - 3
LA  - en
AB  - 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.
KW  - Text mining
KW  - Document classification
KW  - Class-inconsistent data
KW  - Robustness of classification algorithms
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UR  - https://doi.org/10.35377/saucis...1626239
L1  - https://dergipark.org.tr/en/download/article-file/4549073
ER  -