What are the state of art techniques for handling imbalanced data?
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State-of-the-art techniques for handling imbalanced data include resampling techniques and classification algorithms. These techniques aim to address the challenge of imbalanced data by either oversampling the minority class or undersampling the majority class. The effectiveness of these techniques has been evaluated using various machine learning (ML) and deep learning (DL) algorithms. ML methods such as Multilayer Perceptron (MLP), Support Vector Machine (SVM), Naive Bayes (NB), Bayesian Network (BayesNet), J48, and ID3 have shown good performance in achieving balanced accuracy for both classes . DL methods have also provided good performances, especially for undersampled data . Additionally, approaches such as biased learning, class prior models, and one-class classifiers have been used to handle imbalance data and address class rarity issues . These techniques have shown promising results and are expected to play a significant role in various domains in the future .
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| Papers (5) | Insight |
|---|---|
| The paper discusses four paradigms used for handling imbalanced datasets: data level, algorithm level, cost-sensitive, and ensemble methods. | |
08 Dec 2022 | The paper discusses various approaches for handling imbalanced data, including data-level and algorithm-level approaches, as well as the use of one-class classifiers. |
06 Jun 2023 | The paper mentions that there are multiple combinations of resampling and learning methods to handle imbalanced data, but it does not specifically mention the state-of-the-art techniques for handling imbalanced data. |
08 Dec 2022 | The paper discusses various approaches for handling imbalanced data, including data-level and algorithm-level techniques, as well as the use of one-class classifiers. |
| The paper evaluates two main approaches for handling imbalanced datasets: oversampling and undersampling. |
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