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oversampling method FOR ENVIRONMENTAL MONITORING DATA AUGMENTATION IN CADMIUM -POLLUTED PADDY FIELD

INTERNATIONAL JOURNAL OF ROBOTICS & AUTOMATION 2022年第1期37卷 29-36页

作者： Hao, Peitong Li, Yue Xu, Weiman Li, Cong Huo, Mingzhu Zhang, Xiaoyu An, Yi Minist Agr Agroenvironm Protect Inst Tianjin 300071 Peoples R China Nankai Univ Coll Comp Sci Tianjin 300350 Peoples R China Key Lab Med Data Anal & Stat Res Tianjin KLMDASR Tianjin 300350 Peoples R China

Considering cadmium over standard rate (OSR) of Chinese rice not exceeding 10%, accurate identification of cadmium-polluted rice in low OSR regions is more valuable for Cd pollution remediation, relative to in high OSR regions. However, the big gap between the number of positive samples (samples exceeding the cadmium limit in rice) and negative samples (samples not exceeding the cadmium limit in rice) will lead to the collapse of classifier prediction models in low OSR regions. We tried to apply oversampling method to improve the feasibility of the classifier models. The improvement was evaluated by Fl value, which represents the harmonic average of accuracy and recall. Based on the results, oversampling can upgrade the collapsed classifier models (F1 is close to or equal to 0) to the available level (F1 is greater than 0.3). In addition, the stability of six classifier models enhanced by Adaptive Synthetic (ADASYN) and Synthetic Minority oversampling Technique (SMOTE) was significantly improved in low OSR region (coefficient of variation decreased from 156.29% to 12.45% and 7.23%). According to the analysis of correlation coefficient among the parameters in three regions, the high correlation of each index parameter will affect the performance of the classifiers. This method is also suitable and valuable for risk identification of other pollutants, especially considering that pollution regions are always in the minority.

关键词： Cadmium-contaminated rice risk prediction classifier models data augmentation oversampling method

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Instance gravity oversampling method for software defect prediction

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INFORMATION AND SOFTWARE TECHNOLOGY 2025年 179卷

作者： Tang, Yu Zhou, Yang Yang, Cheng Du, Ye Yang, Ming-song Beijing Jiaotong Univ Sch Cyberspace Sci & Technol Beijing 100044 Peoples R China Beijing Jiaotong Univ Beijing Key Lab Secur & Privacy Intelligent Transp Beijing 100044 Peoples R China Beijing Jiaotong Univ Beijing Lab Natl Econ Secur Early Warning Engn Beijing 100044 Peoples R China Beijing Jiaotong Univ Sch Jeme Tienyow Honors Beijing 100044 Peoples R China Beijing Jiaotong Univ Tangshan Res Inst Tangshan 063210 Peoples R China

Context: In the software defect datasets, the number of defective instances is significantly lower than that of nondefective instances. This imbalance adversely impacts the predictive performance of the model. oversampling methods can effectively balance datasets. However, traditional oversampling methods often struggle to capture the underlying relationships between features and are prone to introducing noise during instance synthesis. Objective: Inspired by the law of gravity, we propose a novel oversampling method based on instance gravity (MOSIG). method: This method begins by introducing a new metric, instance gravity, to measure the similarity between instances. Subsequently, feature models are constructed, and instance groups are generated. Instances that meet specific conditions based on instance gravity are then identified within different instance groups. Finally, we propose a novel method for synthesizing defective instances by assigning weights to instances according to their gravity. Results: Experimental results demonstrate that MOSIG significantly enhances the predictive performance of both the CART decision tree and Naive Bayes models across 21 publicly available software defect datasets. The experimental results are further validated using the Friedman ranking and Nemenyi post-hoc test, confirming that MOSIG is statistically significant. Conclusion: MOSIG represents a more promising oversampling method.

关键词： Instance gravity oversampling method Feature model Instance group Software defect prediction

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A Signature-based Assistant Random oversampling method for Malware Detection 18

A Signature-based Assistant Random Oversampling Method for M...

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18th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom) / 13th IEEE International Conference on Big Data Science and Engineering (IEEE BigDataSE)

作者： Pang, Ying Chen, Zhenxiang Peng, Lizhi Ma, Kun Zhao, Chuan Ji, Ke Univ Jinan Prov Key Lab Network Based Intelligent Cpmputing Jinan 250022 Peoples R China

ISBN: (纸本)9781728127767

Mobile malware detection has attracted considerable attention because people are becoming increasingly inseparable from their mobile devices. Some researchers have endeavored to discover traces of malicious apps by harnessing the semantics of network traffic. However, network traffic data naturally exhibit imbalanced distributions. To date, studies that consider such imbalanced problem have been limited. Thus, we propose an oversampling method that can synthesize malicious instances based on a signature from the perspective of nominal features for mobile malware detection problem. First, we cluster the malicious network traffic datasets and generate signatures from each cluster. Second, the signatures are used to synthesize new malicious instances. The synthetic instances combined with instances obtained through the random oversampling method are used to enrich the minority class dataset. Finally, a semantic-based mobile malware detection model is built by using the rebalanced datasets. Our approach is compared with 10 other resampling algorithms on two network traffic datasets with different imbalanced ratios. Results show that the combination of the proposed method and the random oversampling algorithm has superior performance to the 10 other resampling algorithms for imbalanced malware detection.

关键词： malware detection imbalanced learning oversampling method

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A New Segmented oversampling method for Imbalanced Data Classification Using Quasi-Linear SVM

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2017年第6期12卷 891-898页

作者： Zhou, Bo Li, Weite Hu, Jinglu Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Fukuoka 8080135 Japan

Data imbalance occurs on most real-world classification problems and decreases the performance of classifiers. An oversampling method addresses the imbalance problem by generating synthetic samples to balance the data distribution. However, many of the existing oversampling methods have potential problems in generating wrong and unnecessary synthetic samples, which makes the learning tasks difficult. This paper proposes a new segmented oversampling method for imbalanced data classification. The input space is first partitioned into several linearly separable local partitions along the potential separation boundary by introducing a bottom-up, minimal-spanning-tree-based clustering method;an oversampling method is then applied within each local linear partition to prevent the generation of wrong and unnecessary synthetic samples;a quasi-linear support vector machine is finally used to realize the classification by taking advantages of the local linear partitions. Simulation results on different real-world datasets show that the proposed segmented oversampling method is effective for imbalanced data classifications. (C) 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： imbalanced classification support vector machine oversampling method local linear partition kernel composition

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A Synthetic Minority oversampling method Based on Local Densities in Low-Dimensional Space for Imbalanced Learning 1

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20th International Conference on Database Systems for Advanced Applications (DASFAA)

作者： Xie, Zhipeng Jiang, Liyang Ye, Tengju Li, Xiao-Li Fudan Univ Sch Comp Sci Shanghai 200433 Peoples R China Fudan Univ Shanghai Key Lab Data Sci Shanghai 200433 Peoples R China Inst InfoComm Res Singapore Singapore

ISBN: (数字)9783319181233

ISBN: (纸本)9783319181233;9783319181226

Imbalanced class distribution is a challenging problem in many real-life classification problems. Existing synthetic oversampling do suffer from the curse of dimensionality because they rely heavily on Euclidean distance. This paper proposed a new method, called Minority oversampling Technique based on Local Densities in Low-Dimensional Space (or MOT2LD in short). MOT2LD first maps each training sample into a low-dimensional space, and makes clustering of their low-dimensional representations. It then assigns weight to each minority sample as the product of two quantities: local minority density and local majority count, indicating its importance of sampling. The synthetic minority class samples are generated inside some minority cluster. MOT2LD has been evaluated on 15 real-world data sets. The experimental results have shown that our method outperforms some other existing methods including SMOTE, Borderline-SMOTE, ADASYN, and MWMOTE, in terms of G-mean and F-measure.

关键词： Imbalanced learning oversampling method Local densities Dimensionality reduction

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FIAO: Feature Information Aggregation oversampling for imbalanced data classification

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APPLIED SOFT COMPUTING 2024年 161卷

作者： Wang, Fei Zheng, Ming Hu, Xiaowen Li, Hongchao Wang, Taochun Chen, Fulong Anhui Normal Univ Sch Comp & Informat Wuhu 241002 Peoples R China Anhui Normal Univ Anhui Prov Key Lab Ind Intelligence Data Secur Wuhu 241002 Anhui Peoples R China

Classification performance often deteriorates when machine learning algorithms are trained on imbalanced data. Although oversampling methods have been successfully employed to address imbalanced data, existing approaches have limitations such as information loss, difficulty in parameter selection, and boundary effects when using and calculating nearest neighbors and densities. Therefore, this study introduces a novel oversampling method called Feature Information Aggregation oversampling (FIAO). FIAO leverages feature information, including feature importance, feature density, and standard deviation, to guide the oversampling process. Initially, the feature information is employed to partition features into suitable intervals for feature generation. Subsequently, features are generated within these intervals. Finally, the generated features are integrated into the minority class data to achieve effective oversampling. The key advantage of FIAO lies in its ability to fully exploit the intrinsic information carried by the features themselves, thus circumventing issues related to parameter selection and boundary effects. To assess its efficacy, extensive experiments were conducted on 12 widely used benchmark datasets, comparing the performance of the proposed method against 10 popular resampling methods across four commonly used classifiers. The experimental results show that the proposed FIAO method shows ideal results in multiple application scenarios and achieves optimal performance.

关键词： Imbalanced datasets oversampling method Feature information aggregation

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An efficient approach for data-imbalanced hate speech detection in Arabic social media

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第4期45卷 6381-6390页

作者： Mohamed, Mohamed S. Elzayady, Hossam Badran, Khaled M. Salama, Gouda I. Mil Tech Coll Dept Comp Engn & Artificial Intelligence Cairo Egypt

The use of hateful language in public debates and forums is becoming more common. However, this might result in antagonism and conflicts among individuals, which is undesirable in an online environment. Countries, businesses, and educational institutions are exerting their greatest efforts to develop effective solutions to manage this issue. In addition, recognizing such content is difficult, particularly in Arabic, due to a variety of challenges and constraints. Long-tailed data distribution is often one of the most significant issues in actual Arabic hate speech datasets. Pre-trained models, such as bidirectional encoder representations from transformers (BERT) and generative pre-trained transformers (GPT), have become more popular in numerous natural language processing (NLP) applications in recent years. We conduct extensive experiments to address data imbalance issues by utilizing oversampling methods and a focal loss function in addition to traditional loss functions. Quasi-recurrent neural networks (QRNN) are employed to fine-tune the cutting-edge transformerbased models, MARBERTv2, MARBERTv1, and ARBERT. In this context, we suggest a new approach using ensemble learning that incorporates best-performing models for both original and oversampled datasets. Experiments proved that our proposed approach achieves superior performance compared to the most advanced methods described in the literature.

关键词： Text classification Arabic hate speech oversampling method transformers ensemble learning

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Enhancing Deep Learning Model Privacy Against Membership Inference Attacks Using Privacy-Preserving oversampling

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SN Computer Science 2025年第4期6卷 1-21页

作者： Ghosh, Subhasish Mandal, Amit Kr Cortesi, Agostino Department of Computer Science and Engineering SRM University AP Andhra Pradesh Amaravati 522240 India Department of Computer Science Ca’ Foscari University Via Turino 155 Venice 30170 Italy

The overfitting of deep learning models trained using moderately imbalanced datasets is the main factor in increasing the success rate of membership inference attacks. While many oversampling methods have been designed to minimize the data imbalance, only a few defend the deep neural network models against membership inference attacks. We introduce the privacy preserving synthetic minority oversampling technique (PP-SMOTE), that applies privacy preservation mechanisms during data preprocessing rather than the model training phase. The PP-SMOTE oversampling method adds Laplace noise to generate the synthetic data points of minority classes by considering the L1 sensitivity of the dataset. The PP-SMOTE oversampling method demonstrates lower vulnerability to membership inference attacks than the DNN model trained on datasets oversampled by GAN and SVMSMOTE. The PP-SMOTE oversampling method helps retain more model accuracy and lower membership inference attack accuracy compared to the differential privacy mechanisms such as DP-SGD, and DP-GAN. Experimental results showcase that PP-SMOTE effectively mitigates membership inference attack accuracy to approximately below 0.60 while preserving high model accuracy in terms of AUC score approximately above 0.90. Additionally, the broader confidence score distribution achieved by the PP-SMOTE significantly enhances both model accuracy and mitigation of membership inference attacks (MIA). This is confirmed by the loss-epoch curve which shows stable convergence and minimal overfitting during training. Also, the higher variance in confidence scores complicates efforts of attackers to distinguish training data thereby reducing the risk of MIA. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep neural networks Differential privacy Membership inference attack oversampling method

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A data-driven approach to state assessment of the converter valve based on oversampling and Shapley additive explanations

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IET GENERATION TRANSMISSION & DISTRIBUTION 2022年第8期16卷 1607-1619页

作者： Mei, Fei Li, Xuan Zheng, Jianyong Sha, Haoyuan Li, Danqi Hohai Univ Coll Energy & Elect Engn Nanjing Peoples R China Southeast Univ Sch Elect Engn Nanjing Peoples R China

The utilization of data-driven artificial intelligence technology for ultra-high voltage converter valve state assessment can improve efficiency and accuracy, but there are still problems such as imbalance of raw data and poor model interpretability. This paper aims to propose a new type of ultra-high voltage converter valve state assessment method to solve these problems. An oversampling method that considers characteristic boundary information is proposed, which can retain and enhance the boundary information of minority samples. The balanced data are imported into categorical boosting to realize offline training and online evaluation of converter valve state assessment. An interpretability analysis framework of the converter valve state assessment model based on the Shapley additive explanations is proposed, which can explain the operation logic of the model and find the key factors that affect the assessment results. The calculation example shows that the evaluation accuracy of this method can reach up to 98.5%, and the running speed is faster than the conventional method. The results can provide guidance for the maintenance and repair of UHV converter valves.

关键词： UHV converter valves Shapley additive explanations condition monitoring valves power convertors characteristic boundary information converter valve state assessment model offline training online evaluation categorical boosting UHF devices model interpretability data-driven approach ultra-high voltage converter valve state assessment oversampling method power engineering computing data handling operation logic artificial intelligence data-driven artificial intelligence

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An Optimized Asymmetric Pulsewidth Modulation for Sensorless Control of Permanent Magnet Synchronous Machines

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2022年第2期69卷 1389-1399页

作者： Wu, Chun Chen, Zihao Chen, Qiang Zhejiang Univ Technol Coll Informat Engn Hangzhou 310023 Peoples R China

This article proposes an optimized asymmetric pulsewidth modulation (OAPWM) for saliency-based sensorless control of permanent magnet synchronous machines (PMSMs). Compared with the conventional symmetric space vector PWM, OAPWM has merits of longer active vectors and more accurate current slope measurements, which are beneficial to the low-speed sensorless drives. In OAPWM, a distributed zero vector is inserted between two adjacent active vectors. Thus, the current ripple and the power loss can be reduced. Moreover, the length of zero vector is optimized considering the current ripple and modulation index. Then, a current oversampling method and the least square algorithm are adopted to improve the calculation accuracy of the current slope. And the average phase current in one PWM period is calculated using six sampling points. The effectiveness of OAPWM for sensorless control is verified on both 500- and 1500-W PMSMs based on a single field programmable gate array. In comparison with the conventional methods, more accurate position estimation can be achieved during both the transient and steady-state processes.

关键词： Pulse width modulation Sensorless control Voltage control Rotors Field programmable gate arrays Estimation Voltage measurement Least square (LS) algorithm optimized asymmetric pulsewidth modulation (OAPWM) oversampling method permanent magnet synchronous machine (PMSM) sensorless control

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