Improving Requirements Classification with SMOTE-Tomek Preprocessing
Fuente:
arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866914605771522048 |
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| author | Or, Barak |
| author_facet | Or, Barak |
| contents | This study emphasizes the domain of requirements engineering by applying the SMOTE-Tomek preprocessing technique, combined with stratified K-fold cross-validation, to address class imbalance in the PROMISE dataset. This dataset comprises 969 categorized requirements, classified into functional and non-functional types. The proposed approach enhances the representation of minority classes while maintaining the integrity of validation folds, leading to a notable improvement in classification accuracy. Logistic regression achieved 76.16\%, significantly surpassing the baseline of 58.31\%. These results highlight the applicability and efficiency of machine learning models as scalable and interpretable solutions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_06491 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Improving Requirements Classification with SMOTE-Tomek Preprocessing Or, Barak Software Engineering Artificial Intelligence Systems and Control This study emphasizes the domain of requirements engineering by applying the SMOTE-Tomek preprocessing technique, combined with stratified K-fold cross-validation, to address class imbalance in the PROMISE dataset. This dataset comprises 969 categorized requirements, classified into functional and non-functional types. The proposed approach enhances the representation of minority classes while maintaining the integrity of validation folds, leading to a notable improvement in classification accuracy. Logistic regression achieved 76.16\%, significantly surpassing the baseline of 58.31\%. These results highlight the applicability and efficiency of machine learning models as scalable and interpretable solutions. |
| title | Improving Requirements Classification with SMOTE-Tomek Preprocessing |
| topic | Software Engineering Artificial Intelligence Systems and Control |
| url | https://arxiv.org/abs/2501.06491 |