Efficient Handling of Data Imbalance in Health Insurance Fraud Detection Using Meta-Reinforcement Learning by Supriya Seshagiri, K. V. Prema

“…This research is the first to apply Meta-RL to the problem of data imbalance in fraud detection, contributing to a generalizable and efficient framework for imbalanced learning. The findings of this research show that Meta-RL algorithms can be effectively tuned to handle data imbalance without modification to their objective functions and hence, can be considered an appropriate option for health insurance fraud detection solutions.…”
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Multiclass Supervised Learning Approach for SAR-COV2 Severity and Scope Prediction: SC2SSP Framework by Shaik Khasim Saheb, B. Narayanan, T.V. Narayana Rao

“…The model may also utilize preprocessing techniques which are Gaussian smoothing for handling imbalanced data, such as oversampling or under sampling, as well as feature extraction methods such as Local Binary Pattern to identify the most relevant input features for the prediction task. …”
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UAV rice panicle blast detection based on enhanced feature representation and optimized attention mechanism by Shaodan Lin, Deyao Huang, Libin Wu, Zuxin Cheng, Dapeng Ye, Haiyong Weng

“…The incorporation of Focal Tversky Loss further enhances the model's ability to handle imbalanced datasets, improving detection accuracy for rare and severe disease categories. …”
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Prediksi Mahasiswa Drop-Out Di Universitas XYZ by Tubagus Ahmad Marzuqi, Evelline Kristiani, Marcel

“…Prior to model building, the SMOTE method was applied to address the issue of imbalanced data. The results indicate that the predictive model using the Random Forest algorithm achieved an accuracy of 99.67%. …”
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Prediksi Tipe Kepribadian MBTI Artis K-Pop Berdasarkan Caption Instagram Menggunakan Word2Vec dan Long-Short Term Memory (LSTM) by Alfian Hakim, Satrio Hadi Wijoyo, Nanang Yudi Setiawan

“…The distribution of personality types shows that the target is not balanced, so it is necessary to handle imbalanced data, namely shifting the threshold after modeling. …”
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Strategi Penanganan Imbalance Class Pada Model Klasifikasi Penerima Kartu Indonesia Pintar Kuliah Berbasis Neural Network Menggunakan Kombinasi SMOTE dan ENN by Zaqiatud Darojah, Ronny Susetyoko, Nana Ramadijanti

“…This study aims to build the classification model for KIP Kuliah recipients using Neural Network (NN) which can be utilized by universities in selecting prospective KIP Kuliah recipients students. To solve the imbalanced KIP Kuliah recipients data, we propose a hybrid sampling technique that combines the Synthetic Minority Over-Sampling Technique (SMOTE) and the Edited Nearest Neighbor (ENN) and also samples selected deletion method with a new scheme. …”
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Balanced displacement methods and effect evaluation of heterogeneous combination flooding in different reservoirs by Dejun WU, Jian HOU, Lijie LIU, Kang ZHOU, Zhibin AN

“…The remaining oil, extracted remaining oil, oil displacement efficiency, sweep coefficient, planar water saturation variation coefficient, and vertical water absorption imbalanced coefficient are used to evaluate the balanced displacement effects of heterogeneous combination flooding. …”
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