Synthetic Minority Over-sampling Technique (SMOTE) is a popular method for addressing class imbalance in machine learning, which can significantly impact the performance of models and lead to biased predictions. By generating synthetic data for the minority class, SMOTE helps balance the dataset and improve the performance of classification algorithms.
Recent research has explored various modifications and extensions of SMOTE to further enhance its effectiveness. SMOTE-ENC, for example, encodes nominal features as numeric values and can be applied to both mixed datasets and nominal-only datasets. Deep SMOTE adapts the SMOTE idea in deep learning architecture, using a deep neural network regression model to train the inputs and outputs of traditional SMOTE. LoRAS, another oversampling approach, employs Localized Random Affine Shadowsampling to oversample from an approximated data manifold of the minority class, resulting in better ML models in terms of F1-Score and Balanced accuracy.
Generative Adversarial Network (GAN)-based approaches, such as GBO and SSG, have also been proposed to overcome the limitations of existing oversampling methods. These techniques leverage GAN's ability to create almost real samples, improving the performance of machine learning models on imbalanced datasets. Other methods, like GMOTE, use Gaussian Mixture Models to generate instances and adapt tail probability of outliers, demonstrating robust performance when combined with classification algorithms.
Practical applications of SMOTE and its variants can be found in various domains, such as healthcare, finance, and cybersecurity. For instance, SMOTE has been used to generate instances of the minority class in an imbalanced Coronary Artery Disease dataset, improving the performance of classifiers like Artificial Neural Networks, Decision Trees, and Support Vector Machines. In another example, SMOTE has been employed in privacy-preserving integrated analysis across multiple institutions, improving recognition performance and essential feature selection.
In conclusion, SMOTE and its extensions play a crucial role in addressing class imbalance in machine learning, leading to improved model performance and more accurate predictions. As research continues to explore novel modifications and applications of SMOTE, its impact on the field of machine learning is expected to grow, benefiting a wide range of industries and applications.
Synthetic Minority Over-sampling Technique (SMOTE)
Synthetic Minority Over-sampling Technique (SMOTE) Further Reading
1.SMOTE-ENC: A novel SMOTE-based method to generate synthetic data for nominal and continuous features http://arxiv.org/abs/2103.07612v1 Mimi Mukherjee, Matloob Khushi2.Deep Synthetic Minority Over-Sampling Technique http://arxiv.org/abs/2003.09788v1 Hadi Mansourifar, Weidong Shi3.LoRAS: An oversampling approach for imbalanced datasets http://arxiv.org/abs/1908.08346v4 Saptarshi Bej, Narek Davtyan, Markus Wolfien, Mariam Nassar, Olaf Wolkenhauer4.Imbalanced Class Data Performance Evaluation and Improvement using Novel Generative Adversarial Network-based Approach: SSG and GBO http://arxiv.org/abs/2210.12870v1 Md Manjurul Ahsan, Md Shahin Ali, Zahed Siddique5.GMOTE: Gaussian based minority oversampling technique for imbalanced classification adapting tail probability of outliers http://arxiv.org/abs/2105.03855v1 Seung Jee Yang, Kyung Joon Cha6.Another Use of SMOTE for Interpretable Data Collaboration Analysis http://arxiv.org/abs/2208.12458v1 Akira Imakura, Masateru Kihira, Yukihiko Okada, Tetsuya Sakurai7.Investigating the Synthetic Minority class Oversampling Technique (SMOTE) on an imbalanced cardiovascular disease (CVD) dataset http://arxiv.org/abs/2004.04101v1 Ioannis D. Apostolopoulos8.SMOTified-GAN for class imbalanced pattern classification problems http://arxiv.org/abs/2108.03235v2 Anuraganand Sharma, Prabhat Kumar Singh, Rohitash Chandra9.Separation of pulsar signals from noise with supervised machine learning algorithms http://arxiv.org/abs/1704.04659v3 Suryarao Bethapudi, Shantanu Desai10.A Comparison of Synthetic Oversampling Methods for Multi-class Text Classification http://arxiv.org/abs/2008.04636v1 Anna GlazkovaSynthetic Minority Over-sampling Technique (SMOTE) Frequently Asked Questions
What is the Synthetic Minority Over-sampling Technique (SMOTE)?
The Synthetic Minority Over-sampling Technique (SMOTE) is a popular method for addressing class imbalance in machine learning. Class imbalance occurs when the distribution of classes in a dataset is uneven, which can lead to biased predictions and poor model performance. SMOTE generates synthetic data for the minority class, helping to balance the dataset and improve the performance of classification algorithms.
Which algorithms does SMOTE use to create synthetic data?
SMOTE uses a combination of nearest neighbors and interpolation to create synthetic data. It selects a minority class instance and finds its nearest neighbors in the minority class. Then, it generates synthetic instances by interpolating between the selected instance and its neighbors. This process is repeated until the desired level of balance between the majority and minority classes is achieved.
What is the SMOTE sampling technique?
The SMOTE sampling technique is a method for generating synthetic instances of the minority class in an imbalanced dataset. By creating synthetic data, SMOTE helps balance the dataset, which in turn improves the performance of classification algorithms and reduces the impact of class imbalance on model predictions.
How is SMOTE different from random over-sampling?
SMOTE and random over-sampling are both techniques used to address class imbalance in machine learning. While random over-sampling simply duplicates instances of the minority class to balance the dataset, SMOTE generates synthetic instances by interpolating between existing minority class instances and their nearest neighbors. This results in a more diverse and representative sample of the minority class, which can lead to better model performance.
What are some recent advancements and modifications of SMOTE?
Recent research has explored various modifications and extensions of SMOTE, such as SMOTE-ENC, Deep SMOTE, and LoRAS. SMOTE-ENC encodes nominal features as numeric values and can be applied to both mixed datasets and nominal-only datasets. Deep SMOTE adapts the SMOTE idea in deep learning architecture, using a deep neural network regression model to train the inputs and outputs of traditional SMOTE. LoRAS employs Localized Random Affine Shadowsampling to oversample from an approximated data manifold of the minority class, resulting in better ML models in terms of F1-Score and Balanced accuracy.
How do Generative Adversarial Networks (GANs) relate to SMOTE?
Generative Adversarial Networks (GANs) have been proposed as an alternative to SMOTE for addressing class imbalance. GAN-based approaches, such as GBO and SSG, leverage GAN's ability to create almost real samples, improving the performance of machine learning models on imbalanced datasets. These techniques overcome some of the limitations of existing oversampling methods, offering a promising direction for future research.
In which domains can SMOTE and its variants be applied?
SMOTE and its variants have practical applications in various domains, such as healthcare, finance, and cybersecurity. For instance, SMOTE has been used to generate instances of the minority class in an imbalanced Coronary Artery Disease dataset, improving the performance of classifiers like Artificial Neural Networks, Decision Trees, and Support Vector Machines. In another example, SMOTE has been employed in privacy-preserving integrated analysis across multiple institutions, improving recognition performance and essential feature selection.
What is the future direction of SMOTE research?
As research continues to explore novel modifications and applications of SMOTE, its impact on the field of machine learning is expected to grow. Future directions may include the development of new SMOTE variants, the integration of SMOTE with other machine learning techniques, and the application of SMOTE to new domains and industries. By addressing class imbalance and improving model performance, SMOTE and its extensions will continue to benefit a wide range of applications and industries.
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