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parameterized clustering Cleaning Approach for High-Dimensional Datasets with Class Overlap and Imbalance

SN Computer Science 2023年第5期4卷 464页

作者： Goel, Navansh Singaravelu, Mohanapriya Gupta, Shivani Namana, Sriram Singh, Richa Kumar, Ranjeet School of Computer Science Engineering Vellore Institute of Technology Chennai India Department of Computer science and Engineering Birla Institute of Technology Mesra Ranchi India School of Electronics Engineering Vellore Institute of Technology Chennai India

Software Defect Prediction (SDP) uses machine learning algorithms to detect faulty and defective modules inside software projects. Like any machine learning model, the model’s performance depends on the training data. The ongoing research in the field explores various options for improving the quality, but class overlap and imbalance hinder the models’ effectiveness. Unwarranted instances grouped with relevant data act as noise, hindering the classification process. The removal of such instances can lead to improvement in the performance of standard machine learning classification models. This paper proposes a novel parameterized clustering approach, ‘CLARANSCA‘, for cleaning high dimensional datasets suffering from class overlap and imbalance. The parameterized clustering approach ‘CLARANS’ serves as the base for the proposed method. A multi-fold approach to calculating the evaluation metrics is used, ensuring consistency in results. The aggregate results show significant improvements, especially for Recall, AUC, and G-mean values. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： AUC CLARANS CLARANSCA Class imbalance Class overlap G-mean parameterized clustering Recall Software defect prediction

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