Imbalance learning is a challenging task for most standard machine learning algorithms. The Synthetic Minority Oversampling Technique (SMOTE) is a well-known preprocessing approach for handling imbalanced datasets, where the minority class is oversampled by producing synthetic examples in feature vector rather than data space. However, many recent works have shown that the imbalanced ratio in itself is not a problem and deterioration of the model performance is caused by other reasons linked to the minority class sample distribution. The blind oversampling by SMOTE leads to two major problems: noise and borderline examples. Noisy examples are those from one class located in the safe zone of the other. Borderline examples are those located in the neighborhood of the class boundary. These samples are associated with deteriorating performance of the models developed. Therefore, it is critical to concentrate on the minority class data structure and regulate the positioning of the newly introduced minority class samples for better performance of classifiers. Hence, this paper proposes the advanced SMOTE, denoted as A-SMOTE, to adjust the newly introduced minority class examples based on distance to the original minority class samples. To achieve this objective, we first employ the SMOTE algorithm to introduce new samples to the minority and eliminate those examples that are closer to the majority than the minority. We apply the proposed method to 44 datasets at various imbalance ratios. Ten widely used data sampling methods selected from the literature are employed for performance comparison. The C4.5 and Naive Bayes classifiers are utilized for experimental validation. The results confirm the advantage of the proposed method over the other methods in almost all the datasets and illustrate its suitability for data preprocessing in classification tasks.
The object of software defect prediction (SDP) is to identify defect-prone modules. This is achieved through constructing prediction models using datasets obtained by mining software historical depositories. However, data mined from these depositories are often associated with high dimensionality, class imbalance, and mislabels which deteriorate classification performance and increase model complexity. In order to mitigate the consequences, this paper proposes an integrated preprocessing framework in which feature selection (FS), data balance (DB), and noise filtering (NF) techniques are fused to deal with the factors that deteriorate learning performance. We apply the proposed framework on three software metrics, namely static code metric (SCM), object oriented metric (OOM), and combined metric (CombM) and build models based on four scenarios (S): (S1) original data; (S2) FS subsets; (S3) FS subsets after DB using random under sampling (RUS) and synthetic minority oversampling technique (SMOTE); (S4) FS subsets after DB (RUS and SMOTE); and NF using iterative partitioning filter (IPF) and iterative noise filtering based on the fusing of classifiers (INFFC). Empirical results show that 1. the integrated preprocessing of FS, DB, and NF improves the performance of all the models built for SDP, 2. for all FS methods, all the models improve performance progressively from S2 through to S4 in all the software metrics, 3. model performance based on S4 is statistically significantly better than the performance based on S3 for all the software metrics, and 4. in order to achieve optimal model performance for SDP, appropriate implementation of the proposed framework is required. The results also validate the effectiveness of our proposal and provide guidelines for achieving quality training data that enhances model performance for SDP.
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