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imbalanced data Classification Using SVM Based on Improved Simulated Annealing Featuring Synthetic data Generation and Reduction

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Computers, Materials & Continua 2023年第4期75卷 547-564页

作者： Hussein Ibrahim Hussein Said Amirul Anwar Muhammad Imran Ahmad Department of Computer Techniques Engineering AlSafwa University CollegeKarbala56001Iraq Faculty of Electronic Engineering&Technology Universiti Malaysia PerlisArau02600PerlisMalaysia

imbalanced data classification is one of the major problems in machine *** imbalanced dataset typically has significant differences in the number of data samples between its *** most cases,the performance of the machine learning algorithm such as Support Vector Machine(SVM)is affected when dealing with an imbalanced *** classification accuracy is mostly skewed toward the majority class and poor results are exhibited in the prediction of minority-class *** this paper,a hybrid approach combining data pre-processing technique andSVMalgorithm based on improved Simulated Annealing(SA)was ***,the data preprocessing technique which primarily aims at solving the resampling strategy of handling imbalanced datasets was *** this technique,the data were first synthetically generated to equalize the number of samples between classes and followed by a reduction step to remove redundancy and duplicated *** is the training of a balanced dataset using *** this algorithm requires an iterative process to search for the best penalty parameter during training,an improved SA algorithm was proposed for this *** this proposed improvement,a new acceptance criterion for the solution to be accepted in the SA algorithm was introduced to enhance the accuracy of the optimization *** works based on ten publicly available imbalanced datasets have demonstrated higher accuracy in the classification tasks using the proposed approach in comparison with the conventional implementation of *** at an average of 89.65%of accuracy for the binary class classification has demonstrated the good performance of the proposed works.

关键词： imbalanced data resampling technique data reduction support vector machine simulated annealing

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Fault diagnosis of HVAC system with imbalanced data using multi-scale convolution composite neural network

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Building Simulation 2024年第3期17卷 371-386页

作者： Rouhui Wu Yizhu Ren Mengying Tan Lei Nie School of Mechanical Engineering Hubei University of TechnologyWuhan430068China Industrial Research Institute of Xiangyang Hubei University of Technology Xiangyang441100China

Accurate fault diagnosis of heating,ventilation,and air conditioning(HVAC)systems is of significant importance for maintaining normal operation,reducing energy consumption,and minimizing maintenance ***,in practical applications,it is challenging to obtain sufficient fault data for HVAC systems,leading to imbalanced data,where the number of fault samples is much smaller than that of normal ***,most existing HVAC system fault diagnosis methods heavily rely on balanced training sets to achieve high fault diagnosis ***,to address this issue,a composite neural network fault diagnosis model is proposed,which combines SMOTETomek,multi-scale one-dimensional convolutional neural networks(M1DCNN),and support vector machine(SVM).This method first utilizes SMOTETomek to augment the minority class samples in the imbalanced dataset,achieving a balanced number of faulty and normal ***,it employs the M1DCNN model to extract feature information from the augmented ***,it replaces the original Softmax classifier with an SVM classifier for classification,thus enhancing the fault diagnosis *** the SMOTETomek-M1DCNN-SVM method,we conducted fault diagnosis validation on both the ASHRAE RP-1043 dataset and experimental dataset with an imbalance ratio of 1:*** results demonstrate the superiority of this approach,providing a novel and promising solution for intelligent building management,with accuracy and F1 scores of 98.45%and 100%for the RP-1043 dataset and experimental dataset,respectively.

关键词： fault diagnosis chiller imbalanced data SMOTETomek multi-scale neural networks

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Constraint Learning-based Optimal Power Dispatch for Active Distribution Networks with Extremely imbalanced data

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CSEE Journal of Power and Energy Systems 2024年第1期10卷 51-65页

作者： Yonghua Song Ge Chen Hongcai Zhang State Key Laboratory of Internet of Things for Smart City and Department of Electrical and Computer Engineering University of MacaoMacao 999078China School of Electrical and Computer Engineering Purdue UniversityWest LafayetteIN 47907 USA

Transition towards carbon-neutral power systems has necessitated optimization of power dispatch in active distribution networks(ADNs)to facilitate integration of distributed renewable *** to unavailability of network topology and line impedance in many distribution networks,physical model-based methods may not be applicable to their *** tackle this challenge,some studies have proposed constraint learning,which replicates physical models by training a neural network to evaluate feasibility of a decision(i.e.,whether a decision satisfies all critical constraints or not).To ensure accuracy of this trained neural network,training set should contain sufficient feasible and infeasible ***,since ADNs are mostly operated in a normal status,only very few historical samples are ***,the historical dataset is highly imbalanced,which poses a significant obstacle to neural network *** address this issue,we propose an enhanced constraint learning ***,it leverages constraint learning to train a neural network as surrogate of ADN's ***,it introduces Synthetic Minority Oversampling Technique to generate infeasible samples to mitigate imbalance of historical *** incorporating historical and synthetic samples into the training set,we can significantly improve accuracy of neural ***,we establish a trust region to constrain and thereafter enhance reliability of the *** confirm the benefits of the proposed method in achieving desirable optimality and feasibility while maintaining low computational complexity.

关键词： Deep learning demand response distribution networks imbalanced data optimal power flow

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Over-sampling algorithm for imbalanced data classification

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Journal of Systems Engineering and Electronics 2019年第6期30卷 1182-1191页

作者： XU Xiaolong CHEN Wen SUN Yanfei Jiangsu Key Laboratory of Big data Security&Intelligent Processing Nanjing University of Posts and TelecommunicationsNanjing 210023China Institute of Big data Research at Yancheng Nanjing University of Posts and TelecommunicationsYancheng 224000China Office of Scientific R&D Nanjing University of Posts and TelecommunicationsNanjing 210023China

For imbalanced datasets, the focus of classification is to identify samples of the minority class. The performance of current data mining algorithms is not good enough for processing imbalanced datasets. The synthetic minority over-sampling technique(SMOTE) is specifically designed for learning from imbalanced datasets, generating synthetic minority class examples by interpolating between minority class examples nearby. However, the SMOTE encounters the overgeneralization problem. The densitybased spatial clustering of applications with noise(DBSCAN) is not rigorous when dealing with the samples near the *** optimize the DBSCAN algorithm for this problem to make clustering more reasonable. This paper integrates the optimized DBSCAN and SMOTE, and proposes a density-based synthetic minority over-sampling technique(DSMOTE). First, the optimized DBSCAN is used to divide the samples of the minority class into three groups, including core samples, borderline samples and noise samples, and then the noise samples of minority class is removed to synthesize more effective samples. In order to make full use of the information of core samples and borderline samples,different strategies are used to over-sample core samples and borderline samples. Experiments show that DSMOTE can achieve better results compared with SMOTE and Borderline-SMOTE in terms of precision, recall and F-value.

关键词： imbalanced data density-based spatial clustering of applications with noise(DBSCAN) synthetic minority over sampling technique(SMOTE) over-sampling.

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Conditional self-attention generative adversarial network with differential evolution algorithm for imbalanced data classification

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Chinese Journal of Aeronautics 2023年第3期36卷 303-315页

作者： Jiawei NIU Zhunga LIU Quan PAN Yanbo YANG Yang LI Department of Automation Northwestern Polytechnical UniversityXi’an 710072China

imbalanced data classification is an important research topic in real-world applications,like fault diagnosis in an aircraft manufacturing *** over-sampling method is often used to solve this *** generates samples according to the distance between minority ***,the traditional over-sampling method may change the original data distribution,which is harmful to the classification *** this paper,we propose a new method called Conditional SelfAttention Generative Adversarial Network with Differential Evolution(CSAGAN-DE)for imbalanced data *** new method aims at improving the classification performance of minority data by enhancing the quality of the generation of minority *** CSAGAN-DE,the minority data are fed into the self-attention generative adversarial network to approximate the data distribution and create new data for the minority ***,the differential evolution algorithm is employed to automatically determine the number of generated minority data for achieving a satisfactory classification *** experiments are conducted to evaluate the performance of the new CSAGAN-DE *** results show that the new method can efficiently improve the classification performance compared with other related methods.

关键词： Classification Generative adversarial network imbalanced data Optimization Over-sampling

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Fault Diagnosis of Power Transformer Based on Improved ACGAN Under imbalanced data

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Computers, Materials & Continua 2023年第5期75卷 4573-4592页

作者： Tusongjiang.Kari Lin Du Aisikaer.Rouzi Xiaojing Ma Zhichao Liu Bo Li School of Electrical Engineering Xinjiang UniversityUrumqi830047China School of Mathematics Xinjiang UniversityUrumqi830047China

The imbalance of dissolved gas analysis(DGA)data will lead to over-fitting,weak generalization and poor recognition performance for fault diagnosis models based on deep *** handle this problem,a novel transformer fault diagnosis method based on improved auxiliary classifier generative adversarial network(ACGAN)under imbalanced data is proposed in this paper,which meets both the requirements of balancing DGA data and supplying accurate diagnosis *** generator combines one-dimensional convolutional neural networks(1D-CNN)and long short-term memories(LSTM),which can deeply extract the features from DGA samples and be greatly beneficial to ACGAN’s data balancing and fault *** discriminator adopts multilayer perceptron networks(MLP),which prevents the discriminator from losing important features of DGA data when the network is too complex and the number of layers is too *** experimental results suggest that the presented approach can effectively improve the adverse effects of DGA data imbalance on the deep learning models,enhance fault diagnosis performance and supply desirable diagnosis accuracy up to 99.46%.Furthermore,the comparison results indicate the fault diagnosis performance of the proposed approach is superior to that of other conventional ***,the method presented in this study has excellent and reliable fault diagnosis performance for various unbalanced *** addition,the proposed approach can also solve the problems of insufficient and imbalanced fault data in other practical application fields.

关键词： Power transformer dissolved gas analysis imbalanced data auxiliary classifier generative adversarial network

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Machine Learning and Synthetic Minority Oversampling Techniques for imbalanced data: Improving Machine Failure Prediction

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Computers, Materials & Continua 2023年第6期75卷 4821-4841页

作者： Yap Bee Wah Azlan Ismail Nur Niswah Naslina Azid Jafreezal Jaafar Izzatdin Abdul Aziz Mohd Hilmi Hasan Jasni Mohamad Zain Institute for Big data Analytics and Artificial Intelligence(IBDAAI) Kompleks Al-KhawarizmiUniversiti Teknologi MARA(UiTM)Shah Alam40450SelangorMalaysia School of Computing Sciences College of ComputingInformatics and MediaUniversiti Teknologi MARA(UiTM)40450Shah AlamSelangorMalaysia Mathematical Sciences Studies College of ComputingInformatics and MediaUniversiti Teknologi MARA(UiTM)Kelantan BranchMachang CampusBukit Ilmu18500MachangKelantan Darul NaimMalaysia Centre for Research in data Science(CeRDaS) Department of Computer and Information Sciences(DCIS)Universiti Teknologi PETRONAS(UTP)Seri Iskandar32610PerakMalaysia UNITAR International University Jalan SS6/3SS6Petaling Jaya47301SelangorMalaysia

Prediction of machine failure is challenging as the dataset is often imbalanced with a low failure *** common approach to han-dle classification involving imbalanced data is to balance the data using a sampling approach such as random undersampling,random oversampling,or Synthetic Minority Oversampling Technique(SMOTE)*** paper compared the classification performance of three popular classifiers(Logistic Regression,Gaussian Naïve Bayes,and Support Vector Machine)in predicting machine failure in the Oil and Gas *** original machine failure dataset consists of 20,473 hourly data and is imbalanced with 19945(97%)‘non-failure’and 528(3%)‘failure data’.The three independent variables to predict machine failure were pressure indicator,flow indicator,and level *** accuracy of the classifiers is very high and close to 100%,but the sensitivity of all classifiers using the original dataset was close to *** performance of the three classifiers was then evaluated for data with different imbalance rates(10%to 50%)generated from the original data using SMOTE,SMOTE-Support Vector Machine(SMOTE-SVM)and SMOTE-Edited Nearest Neighbour(SMOTE-ENN).The classifiers were evaluated based on improvement in sensitivity and *** showed that the sensitivity of all classifiers increases as the imbalance rate *** with radial basis function(RBF)kernel has the highest sensitivity when data is balanced(50:50)using SMOTE(Sensitivitytest=0.5686,Ftest=0.6927)compared to Naïve Bayes(Sensitivitytest=0.4033,Ftest=0.6218)and Logistic Regression(Sensitivitytest=0.4194,Ftest=0.621).Overall,the Gaussian Naïve Bayes model consistently improves sensitivity and F-measure as the imbalance ratio increases,but the sensitivity is below 50%.The classifiers performed better when data was balanced using SMOTE-SVM compared to SMOTE and SMOTE-ENN.

关键词： Machine failure machine learning imbalanced data SMOTE classification

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An interpretability model for syndrome differentiation of HBV-ACLF in traditional Chinese medicine using small-sample imbalanced data

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Digital Chinese Medicine 2024年第2期7卷 137-147页

作者： ZHOU Zhan PENG Qinghua XIAO Xiaoxia ZOU Beiji LIU Bin GUO Shuixia School of Informatics Hunan University of Chinese MedicineChangshaHunan 410208China School of Traditional Chinese Medicine Hunan University of Chinese MedicineChangshaHunan 410208China School of Mathematics and Statistics Hunan Normal UniversityChangshaHunan 410081China

Objective Clinical medical record data associated with hepatitis B-related acute-on-chronic liver failure(HBV-ACLF)generally have small sample sizes and a class ***,most machine learning models are designed based on balanced data and lack *** study aimed to propose a traditional Chinese medicine(TCM)diagnostic model for HBV-ACLF based on the TCM syndrome differentiation and treatment theory,which is clinically interpretable and highly *** We collected medical records from 261 patients diagnosed with HBV-ACLF,including three syndromes:Yang jaundice(214 cases),Yang-Yin jaundice(41 cases),and Yin jaundice(6 cases).To avoid overfitting of the machine learning model,we excluded the cases of Yin *** data standardization and cleaning,we obtained 255 relevant medical records of Yang jaundice and Yang-Yin *** address the class imbalance issue,we employed the oversampling method and five machine learning methods,including logistic regression(LR),support vector machine(SVM),decision tree(DT),random forest(RF),and extreme gradient boosting(XGBoost)to construct the syndrome diagnosis *** study used precision,F1 score,the area under the receiver operating characteristic(ROC)curve(AUC),and accuracy as model evaluation *** model with the best classification performance was selected to extract the diagnostic rule,and its clinical significance was thoroughly ***,we proposed a novel multiple-round stable rule extraction(MRSRE)method to obtain a stable rule set of features that can exhibit the model’s clinical *** The precision of the five machine learning models built using oversampled balanced data exceeded *** these models,the accuracy of RF classification of syndrome types was 0.92,and the mean F1 scores of the two categories of Yang jaundice and Yang-Yin jaundice were 0.93 and 0.94,***,the AUC was *** extraction rules of the RF syn

关键词： Traditional Chinese medicine(TCM) Hepatitis B-related acute-on-chronic liver failure(HBV-ACLF) imbalanced data Random forest(RF) Interpretability

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A Research on Rebalance Algorithm for imbalanced data Based on Probability Graph

A Research on Rebalance Algorithm for Imbalanced Data Based ...

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第32届中国控制与决策会议

作者： Gang Chen Tengyan Li School of Science Dalian Maritime University

In imbalanced data,the results of classification are often biased toward the majority class,which reduces the classified accuracy of the *** on this,this paper proposes a probability graph rebalancing algorithm based on Gaussian Mixture Model-EM（GMM-EM） algorithm for imbalanced *** probability density functions of the minority class and majority class are obtained by the GMM-EM *** according to the probability graph（μ-σ graph） of the dataset,the security concept of the minority class is put out,which determines the different generating weights of minority *** that,oversampling is performed using the new algorithm *** proposed method not only considers the generating direction of minority class,but also ensures the consistency of the probability distribution of the data before and after the ***,the balanced dataset is classified using the decision tree *** results show that the new algorithm is more efficient than other existing methods.

关键词： imbalanced data Security Generating direction Probability graph

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A New Combination Sampling Method for imbalanced data

A New Combination Sampling Method for Imbalanced Data

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2013年中国智能自动化学术会议

作者： Hu Li Peng Zou Xiang Wang Rongze Xia School of Computer National University of Defense Technology

imbalanced data is commonly in the real world and brings a lot of *** this paper,we propose a combination sampling method which resamples both minority class and majority *** SMOTE(ISMOTE) is used to do over-sampling on minority class,while distance-based under-sampling (DUS) method is used to do under-sampling on majority *** adjust the sampling times to search for the optimal results while maintain the dataset size *** on UCI datasets show that the proposed method performs better than using single over-sampling or under-sampling method.

关键词： imbalanced data Under-sampling Over-sampling SMOTE

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