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.

翻译：本研究聚焦需求工程领域，通过应用SMOTE-Tomek预处理技术并结合分层K折交叉验证，解决PROMISE数据集中的类别不平衡问题。该数据集包含969条已分类需求，分为功能性和非功能性两类。所提方法在保持验证折完整性的同时增强了少数类的表示，从而显著提升分类准确率。逻辑回归模型达到76.16%的准确率，大幅超越58.31%的基准值。这些结果凸显了机器学习模型作为可扩展且可解释方案的有效性与适用性。