Selecting techniques is a crucial element of the business analysis approach planning in IT projects. Particular attention is paid to the choice of techniques for requirements elicitation. One of the promising methods for selecting techniques is using machine learning algorithms trained on the practitioners' experience considering different projects' contexts. The effectiveness of ML models is significantly affected by the balance of the training dataset, which is violated in the case of popular techniques. The paper aims to analyze the efficiency of the Synthetic Minority Over-sampling Technique usage in Machine Learning models for elicitation technique selection in case of the imbalanced training dataset and possible ways for positive feature importance selection. The computational experiment results confirmed the effectiveness of using the proposed approaches to improve the accuracy of machine learning models for selecting requirements elicitation techniques. Proposed approaches can be used to build Machine Learning models for business analysis activities planning in IT projects.

翻译：选择技术是IT项目中业务分析方法规划的关键要素。特别关注的是对需求获取技术的选择。选择技术的一种有前途的方法是使用机器学习算法，考虑不同项目背景下从从业者的经验进行训练。机器学习模型的有效性受到训练数据集的平衡的影响，这在使用流行技术的情况下也被违反了。本文旨在分析在不平衡的训练数据集情况下，合成少数类过采样技术在机器学习模型中使用规则获取技术选择的效率，以及正特征重要性选择的可能方法。计算实验结果证实了使用所提出的方法提高机器学习模型在选择需求获取技术方面的准确性的效果。所提出的方法可用于构建IT项目中业务分析活动规划的机器学习模型。