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The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is considered “de facto” standard in the framework of learning from imbalanced data. This is due to its simplicity in the design of the procedure, as well as its robustness when applied to different type of problems. Since its publication in 2002, SMOTE has proven success...
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... section is devoted to enumerate and categorize them according to the properties studied before. Table 1 presents an enumeration of the methods reviewed in this paper. In this field, it is usual that the authors provide a name for their proposal, with a few exceptions. ...
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... we can see in Table 1, the most frequent properties exploited by the techniques are the initial selection and adaptive generation of synthetic examples. Filtering is becoming more common in recent years, as well as the use of kernel functions. ...
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... to space limitations, it is not possible to describe all the reviewed techniques. Nevertheless, we will provide brief explanations for the most well-known techniques from Table 1: • Borderline-SMOTE ( Han et al., 2005): This algorithm draws from the premise of that the examples far from the borderline may contribute little to the classification success. Thus, the technique indentifies those examples which belong to the borderline by using the ratio between the majority and minority examples within the neighborhood of each instance to be oversampled. ...
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... 2 shows a list of ensemble based techniques that incorporate SMOTE itself or a derivative of SMOTE as a major step to achieve the diversity of the set of classifiers learned to form the ensemble. Note that this table only contains the methods concerned with The structure of the Table 2 is very similar to the previous one, Table 1. The dimensionality change and filtering are two properties not used in ensembles. ...
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... section is devoted to enumerate and categorize them according to the properties studied before. Table 1 presents an enumeration of the methods reviewed in this paper. In this field, it is usual that the authors provide a name for their proposal, with a few exceptions. ...
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... we can see in Table 1, the most frequent properties exploited by the techniques are the initial selection and adaptive generation of synthetic examples. Filtering is becoming more common in recent years, as well as the use of kernel functions. ...
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... to space limitations, it is not possible to describe all the reviewed techniques. Nevertheless, we will provide brief explanations for the most well-known techniques from Table 1: • Borderline-SMOTE ( Han et al., 2005): This algorithm draws from the premise of that the examples far from the borderline may contribute little to the classification success. Thus, the technique indentifies those examples which belong to the borderline by using the ratio between the majority and minority examples within the neighborhood of each instance to be oversampled. ...
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... 2 shows a list of ensemble based techniques that incorporate SMOTE itself or a derivative of SMOTE as a major step to achieve the diversity of the set of classifiers learned to form the ensemble. Note that this table only contains the methods concerned with The structure of the Table 2 is very similar to the previous one, Table 1. The dimensionality change and filtering are two properties not used in ensembles. ...
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... section is devoted to enumerate and categorize them according to the properties studied before. Table 1 presents an enumeration of the methods reviewed in this paper. In this field, it is usual that the authors provide a name for their proposal, with a few exceptions. ...
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... we can see in Table 1, the most frequent properties exploited by the techniques are the initial selection and adaptive generation of synthetic examples. Filtering is becoming more common in recent years, as well as the use of kernel functions. ...
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... to space limitations, it is not possible to describe all the reviewed techniques. Nevertheless, we will provide brief explanations for the most well-known techniques from Table 1: • Borderline-SMOTE ( Han et al., 2005): This algorithm draws from the premise of that the examples far from the borderline may contribute little to the classifica- tion success. Thus, the technique indentifies those examples which belong to the borderline by using the ratio between the majority and minority examples within the neighborhood of each instance to be oversampled. ...
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... 2 shows a list of ensemble based techniques that incorporate SMOTE itself or a derivative of SMOTE as a major step to achieve the diversity of the set of classifiers learned to form the ensemble. Note that this table only contains the methods concerned with The structure of the Table 2 is very similar to the previous one, Table 1. The dimen- sionality change and filtering are two properties not used in ensembles. ...
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Citations
... There are two key problems existing during the procedure. One is how to determine an appropriate k value; the other is how to select the nearest neighbors as candidate assistance reference sample set (CAR) for PR [2,[12][13][14]. More details are discussed below. ...
... (a) To verify the validity of HNaNE, several SMOTE methods with different k-parameters are used as a comparison. The k-parameters are set according to the recommendations in [14], ranging from 5 to 10. (b) To validate HNaNSMOTE, SMOTE with k = λ (i.e., SMNaNE and SMHNaNE) is also utilized for comparison. ...
... In high-dimensional datasets, interpolation-based oversampling approaches are frequently paired with sparse strategies such as dimensionality reduction to mitigate the detrimental impacts of the curse of dimensionality [7,14]. In Experiment C, sparse PCA is applied to reduce the dimensions of 13 high-dimensional datasets. ...
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The occurrence of class-imbalanced datasets is a frequent observation in natural science research, emphasizing the paramount importance of effectively harnessing them to construct highly accurate models for rockburst prediction. Initially, genuine rockburst incidents within a burial depth of 500 m were sourced from literature, revealing a small dataset imbalance issue. Utilizing various mainstream oversampling techniques, the dataset was expanded to generate six new datasets, subsequently subjected to 12 classifiers across 84 classification processes. The model incorporating the highest-scoring model from the original dataset and the top two models from the expanded dataset, yielded a high-performance model. Findings indicate that the KMeansSMOTE oversampling technique exhibits the most substantial enhancement across the combined 12 classifiers, whereas individual classifiers favor ET+SVMSMOTE and RF+SMOTENC. Following multiple rounds of hyper parameter adjustment via random cross-validation, the ET+SVMSMOTE combination attained the highest accuracy rate of 93.75%, surpassing mainstream models for rockburst prediction. Moreover, the SVMSMOTE technique, augmenting samples with fewer categories, demonstrated notable benefits in mitigating overfitting, enhancing generalization, and improving Recall and F1 score within RF classifiers. Validated for its high generalization performance, accuracy, and reliability. This process also provides an efficient framework for model development.
... Examples include its use in education [102], data mining [68], healthcare [50], finance [120], and environmental studies [51]. The application of SMOTE for addressing class imbalance problems has expanded across various fields, resulting in the development of several SMOTE variants over the years [31]. Notable variants include SMOTE-ENN, K-Means SMOTE, SMOTE-SVM, Borderline SMOTE, Geometric SMOTE, and Weighted SMOTE [55]. ...
... This process generates random points along the "line segments" connecting the selected minority instance and its neighbors. The procedure is repeated for various samples in the minority class until the desired number of synthetic samples is produced [31]. Studies have demonstrated that combining SMOTE with under-sampling techniques results in more robust performance compared to using SMOTE alone [20,106,51]. ...
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... The data were prepared through balancing and normalization techniques to ensure data quality during model training. For data balancing, a combination of undersampling and SMOTE (synthetic minority over-sampling technique) [33] was applied. Initially, undersampling was used to reduce the number of incorrect samples. ...
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... The Synthetic Minority Oversampling Technique (SMOTE) is a widely-used ML method that addresses this issue [21]. SMOTE creates a balanced dataset by oversampling minority instances, enhancing machine learning models' performance on imbalanced data using a simple and efficient augmentation technique [22]. This work examines the use of ML algorithms to create predictive models for ICU admission, using an extensive array of clinical and laboratory characteristics in COVID-19 patients, including variables that differentiate between those with and without diabetes. ...
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... Conversely, over-sampling aims to augment the class size of minority groups in order to prevent the loss of knowledge. Synthetic minority over-sampling technique (SMOTE) constructs synthetic examples of the minority class, thus minimizing the possibility of overfitting (Torgo et al. 2013;Fernandez et al. 2018). In this study, our training set contains 16,364 positive and 22,338 negative instances, thus suffering a class imbalance. ...
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... SMOTE generates synthetic samples by interpolating between existing minority class examples, effectively mitigating overfitting without discarding important data [70]. Although more sophisticated algorithms like Borderline-SMOTE [71], ADASYN [72], and SMOTEENN [73] offer refined resampling processes, they introduce additional complexity and computational overhead [74]. Additionally, its proven efficacy in SFP scenarios further supports our choice. ...
In software development, Software Fault Prediction (SFP) is essential for optimising resource allocation and improving testing efficiency. Traditional SFP methods typically use binary-class models, which can provide a limited perspective on the varying risk levels associated with individual software modules. This study explores the impacts of Error-type Metrics on the fault-proneness of software modules in domain-specific software projects. Also, it aims to enhance SFP methods by introducing a risk-based approach using Error-type Metrics. This method categorises software modules into High, Medium, and Low-Risk categories, offering a more granular and informative fault prediction framework. This approach aims to refine the fault prediction process and contribute to more effective resource allocation and project management in software development. We explore the domain-specific impact of Error-type Metrics through Principal Component Analysis (PCA), aiming to fill a gap in the existing literature by offering insights into how these metrics affect machine learning models across different software domains. We employ three machine learning models - Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGB) - to test our approach. The Synthetic Minority Over-sampling Technique (SMOTE) is used to address class imbalance. Our methodology is validated on fault data from four open-source software projects, aiming to confirm the robustness and generalisability of our approach. The PCA findings provide evidence of the varied impacts of Error-type Metrics in different software environments. Comparative analysis indicates a strong performance by the XGB model, achieving an accuracy of 97.4%, a Matthews Correlation Coefficient of 96.1%, and an F1-score of 97.4% across the datasets. These results suggest the potential of the proposed method to contribute to software testing and quality assurance practices. Our risk-based SFP approach introduces a new perspective to risk assessment in software development. The study’s findings contribute insights into the domain-specific applicability of Error-type Metrics, expanding their potential utility in SFP. Future research directions include refining our fault-counting methodology and exploring broader applications of Error-type Metrics and our proposed risk-based approach.
