Majority-to-minority resampling for boosting-based classification under imbalanced data

Wang, Gaoshan; Wang, Jian; He, Kejing

doi:10.1007/s10489-022-03585-2

Cited by 10 publications

(5 citation statements)

References 67 publications

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“…This is because classifiers can become biased towards the majority group, potentially overlooking or inaccurately classifying instances from the less-represented category. Ensuring accurate classification in these circumstances is crucial to avoiding misleading results [20], [21]. The characteristics of the dataset can exacerbate the issue of having insufficient training observations, which in turn can lead to overfitting.…”

Section: Crowd Scene Classification Using Fully Connected Deep Neural...mentioning

confidence: 99%

Motion Pattern-Based Scene Classification Using Adaptive Synthetic Oversampling and Fully Connected Deep Neural Network

Mohammed,

Al-Dhamari,

Saeed

et al. 2023

IEEE Access

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Analyzing crowded environments has become an increasingly researched topic in computer vision community, largely due to its myriad practical applications, including enhanced video surveillance systems and the estimation of crowd density in specific settings. This paper presents a comprehensive method for progressing the study of crowd dynamics and behavioral analysis, specifically focusing on the classification of movement patterns. We introduce a specialized neural network-based classifier explicitly designed for the accurate categorization of various crowd scenes. This classifier fills a unique niche in the existing literature by offering robust and adaptive classification capabilities. To optimize the performance of our model, we conduct an in-depth analysis of loss functions commonly employed in multi-class classification tasks. Our study encompasses four widely used loss functions: Focal Loss, Huber Loss, Cross-Entropy Loss, and Multi-Margin Loss. Based on empirical findings, we introduce a Joint Loss function that combines the strengths of Cross-Entropy and Multi-Margin Loss, outperforming existing methods across key performance metrics such as accuracy, precision, recall, and F1-score. Furthermore, we address the critical challenge of class imbalance in motion patterns within crowd scenes. To this end, we perform a comprehensive comparative study of two leading oversampling techniques: the synthetic minority oversampling technique (SMOTE) and adaptive synthetic sampling (ADASYN). Our results indicate that ADASYN is superior at enhancing classification performance. This approach not only mitigates the issue of class imbalance but also provides robust empirical validation for our proposed method. Finally, we subject our model to a rigorous evaluation using the Collective Motion Database, facilitating a comprehensive comparison with existing state-of-the-art techniques. This evaluation confirms the effectiveness of our model and aligns it with established paradigms in the field.

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Section: Crowd Scene Classification Using Fully Connected Deep Neural...mentioning

confidence: 99%

Motion Pattern-Based Scene Classification Using Adaptive Synthetic Oversampling and Fully Connected Deep Neural Network

Mohammed,

Al-Dhamari,

Saeed

et al. 2023

IEEE Access

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“…Wang et al [26] introduced a hybrid strategy called Majority-to-Minority Resampling (MMR) and a boosting algorithm called Majority-to-Minority Boosting (MMBoost) for classifcation tasks. MMR was developed to tackle class imbalance by taking samples from the majority class to augment the minority class.…”

Section: Related Workmentioning

confidence: 99%

IMBoost: A New Weighting Factor for Boosting to Improve the Classification Performance of Imbalanced Data

Roshan,

Tanha,

Hallaji

et al. 2023

Complexity

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Imbalanced datasets pose significant challenges in the field of machine learning, as they consist of samples where one class (majority) dominates over the other class (minority). Although AdaBoost is a popular ensemble method known for its good performance in addressing various problems, it fails when dealing with imbalanced data sets due to its bias towards the majority class samples. In this study, we propose a novel weighting factor to enhance the performance of AdaBoost (called IMBoost). Our approach involves computing weights for both minority and majority class samples based on the performance of classifier on each class individually. Subsequently, we resample the data sets according to these new weights. To evaluate the effectiveness of our method, we compare it with six well-known ensemble methods on 30 imbalanced data sets and 4 synthetic data sets using ROC, precision-eecall AUC, and G-mean metrics. The results demonstrate the superiority of IMBoost. To further analyze the performance, we employ statistical tests, which confirm the excellence of our method.

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“…Where 𝑂 𝑗 is the output value in unit j and 𝐼 𝑗 is the input value in j-unit. Next, calculate the error value used as a stop condition using the MSE calculation formula as in (9).…”

Section: Calculate the Information Gain Ratio For Each Feature I In Fmentioning

confidence: 99%

“…Related research on using the random oversampling and SMOOTE technique outperformed the other resampling [8]. Majorityto-Minority Resampling (MMR), a hybrid approach to pick switched instances, adaptively selects potential instances from the majority class to enhance the minority class, showing that the result of the proposed approach outperforms several strong baselines across standard metrics for imbalanced data [9]. Similarity Oversampling and Undersampling Preprocessing (SOUP), which resamples tough cases, outperforms specialist preprocessing methods for multi-imbalanced issues and competes with the most famous decomposition ensembles on natural and artificial datasets [10].…”

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confidence: 99%

The Effect of Resampling on Classifier Performance: an Empirical Study

Pujianto,

Akbar,

Lassela

et al. 2022

Knowl. Eng. Data Sci.

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An imbalanced class on a dataset is a common classification problem. The effect of using imbalanced class datasets can cause a decrease in the performance of the classifier. Resampling is one of the solutions to this problem. This study used 100 datasets from 3 websites: UCI Machine Learning, Kaggle, and OpenML. Each dataset will go through 3 processing stages: the resampling process, the classification process, and the significance testing process between performance evaluation values of the combination of classifier and the resampling using paired t-test. The resampling used in the process is Random Undersampling, Random Oversampling, and SMOTE. The classifier used in the classification process is Naïve Bayes Classifier, Decision Tree, and Neural Network. The classification results in accuracy, precision, recall, and f-measure values are tested using paired t-tests to determine the significance of the classifier's performance from datasets that were not resampled and those that had applied the resampling. The paired t-test is also used to find a combination between the classifier and the resampling that gives significant results. This study obtained two results. The first result is that resampling on imbalanced class datasets can substantially affect the classifier's performance more than the classifier's performance from datasets that are not applied the resampling technique. The second result is that combining the Neural Network Algorithm without the resampling provides significance based on the accuracy value. Combining the Neural Network Algorithm with the SMOTE technique provides significant performance based on the amount of precision, recall, and f-measure.

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Majority-to-minority resampling for boosting-based classification under imbalanced data

Cited by 10 publications

References 67 publications

Motion Pattern-Based Scene Classification Using Adaptive Synthetic Oversampling and Fully Connected Deep Neural Network

Motion Pattern-Based Scene Classification Using Adaptive Synthetic Oversampling and Fully Connected Deep Neural Network

IMBoost: A New Weighting Factor for Boosting to Improve the Classification Performance of Imbalanced Data

The Effect of Resampling on Classifier Performance: an Empirical Study

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