An Imbalanced Data Classification Method Based on Hybrid Resampling and Fine Cost Sensitive Support Vector Machine

When building a classification model,the scenario where the samples of one class are significantly more than those of the other class is called data imbalance.Data imbalance causes the trained classification model to be in favor of the majority class(usually defined as the negative class),which may do harm to the accuracy of the minority class(usually defined as the positive class),and then lead to poor overall performance of the model.A method called MSHR-FCSSVM for solving imbalanced data classification is proposed in this article,which is based on a new hybrid resampling approach(MSHR)and a new fine cost-sensitive support vector machine(CS-SVM)classifier(FCSSVM).The MSHR measures the separability of each negative sample through its Silhouette value calculated by Mahalanobis distance between samples,based on which,the so-called pseudo-negative samples are screened out to generate new positive samples(over-sampling step)through linear interpolation and are deleted finally(under-sampling step).This approach replaces pseudo-negative samples with generated new positive samples one by one to clear up the inter-class overlap on the borderline,without changing the overall scale of the dataset.The FCSSVM is an improved version of the traditional CS-SVM.It considers influences of both the imbalance of sample number and the class distribution on classification simultaneously,and through finely tuning the class cost weights by using the efficient optimization algorithm based on the physical phenomenon of rime-ice(RIME)algorithm with cross-validation accuracy as the fitness function to accurately adjust the classification borderline.To verify the effectiveness of the proposed method,a series of experiments are carried out based on 20 imbalanced datasets including both mildly and extremely imbalanced datasets.The experimental results show that the MSHR-FCSSVM method performs better than the methods for comparison in most cases,and both the MSHR and the FCSSVM played significant roles.

Cost-Sensitive Dual-Stream Residual Networks for Imbalanced Classification

Imbalanced data classification is the task of classifying datasets where there is a significant disparity in the number of samples between different classes.This task is prevalent in practical scenarios such as industrial fault diagnosis,network intrusion detection,cancer detection,etc.In imbalanced classification tasks,the focus is typically on achieving high recognition accuracy for the minority class.However,due to the challenges presented by imbalanced multi-class datasets,such as the scarcity of samples in minority classes and complex inter-class relationships with overlapping boundaries,existing methods often do not perform well in multi-class imbalanced data classification tasks,particularly in terms of recognizing minority classes with high accuracy.Therefore,this paper proposes a multi-class imbalanced data classification method called CSDSResNet,which is based on a cost-sensitive dualstream residual network.Firstly,to address the issue of limited samples in the minority class within imbalanced datasets,a dual-stream residual network backbone structure is designed to enhance the model’s feature extraction capability.Next,considering the complexities arising fromimbalanced inter-class sample quantities and imbalanced inter-class overlapping boundaries in multi-class imbalanced datasets,a unique cost-sensitive loss function is devised.This loss function places more emphasis on the minority class and the challenging classes with high interclass similarity,thereby improving the model’s classification ability.Finally,the effectiveness and generalization of the proposed method,CSDSResNet,are evaluated on two datasets:‘DryBeans’and‘Electric Motor Defects’.The experimental results demonstrate that CSDSResNet achieves the best performance on imbalanced datasets,with macro_F1-score values improving by 2.9%and 1.9%on the two datasets compared to current state-of-the-art classification methods,respectively.Furthermore,it achieves the highest precision in single-class recognition tasks for the minority class.

A Novel Framework for Learning and Classifying the Imbalanced Multi-Label Data

A generalization of supervised single-label learning based on the assumption that each sample in a dataset may belong to more than one class simultaneously is called multi-label learning.The main objective of this work is to create a novel framework for learning and classifying imbalancedmulti-label data.This work proposes a framework of two phases.The imbalanced distribution of themulti-label dataset is addressed through the proposed Borderline MLSMOTE resampling method in phase 1.Later,an adaptive weighted l21 norm regularized(Elastic-net)multilabel logistic regression is used to predict unseen samples in phase 2.The proposed Borderline MLSMOTE resampling method focuses on samples with concurrent high labels in contrast to conventional MLSMOTE.The minority labels in these samples are called difficult minority labels and are more prone to penalize classification performance.The concurrentmeasure is considered borderline,and labels associated with samples are regarded as borderline labels in the decision boundary.In phase II,a novel adaptive l21 norm regularized weighted multi-label logistic regression is used to handle balanced data with different weighted synthetic samples.Experimentation on various benchmark datasets shows the outperformance of the proposed method and its powerful predictive performances over existing conventional state-of-the-art multi-label methods.

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

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 imbalance.However,most machine learning models are designed based on balanced data and lack interpretability.This 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 accurate.Methods 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 jaundice.After data standardization and cleaning,we obtained 255 relevant medical records of Yang jaundice and Yang-Yin jaundice.To 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 models.This study used precision,F1 score,the area under the receiver operating characteristic(ROC)curve(AUC),and accuracy as model evaluation metrics.The model with the best classification performance was selected to extract the diagnostic rule,and its clinical significance was thoroughly analyzed.Furthermore,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 interpretability.Results The precision of the five machine learning models built using oversampled balanced data exceeded 0.90.Among 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,respectively.Additionally,the AUC was 0.98.The extraction rules of the RF syndrome differentiation model based on the MRSRE method revealed that the common features of Yang jaundice and Yang-Yin jaundice were wiry pulse,yellowing of the urine,skin,and eyes,normal tongue body,healthy sublingual vessel,nausea,oil loathing,and poor appetite.The main features of Yang jaundice were a red tongue body and thickened sublingual vessels,whereas those of Yang-Yin jaundice were a dark tongue body,pale white tongue body,white tongue coating,lack of strength,slippery pulse,light red tongue body,slimy tongue coating,and abdominal distension.This is aligned with the classifications made by TCM experts based on TCM syndrome differentiation and treatment theory.Conclusion Our model can be utilized for differentiating HBV-ACLF syndromes,which has the potential to be applied to generate other clinically interpretable models with high accuracy on clinical data characterized by small sample sizes and a class imbalance.

A Stacked Ensemble Deep Learning Approach for Imbalanced Multi-Class Water Quality Index Prediction

A common difficulty in building prediction models with real-world environmental datasets is the skewed distribution of classes.There are significantly more samples for day-to-day classes,while rare events such as polluted classes are uncommon.Consequently,the limited availability of minority outcomes lowers the classifier’s overall reliability.This study assesses the capability of machine learning(ML)algorithms in tackling imbalanced water quality data based on the metrics of precision,recall,and F1 score.It intends to balance the misled accuracy towards the majority of data.Hence,10 ML algorithms of its performance are compared.The classifiers included are AdaBoost,SupportVector Machine,Linear Discriminant Analysis,k-Nearest Neighbors,Naive Bayes,Decision Trees,Random Forest,Extra Trees,Bagging,and the Multilayer Perceptron.This study also uses the Easy Ensemble Classifier,Balanced Bagging,andRUSBoost algorithm to evaluatemulti-class imbalanced learning methods.The comparison results revealed that a highaccuracy machine learning model is not always good in recall and sensitivity.This paper’s stacked ensemble deep learning(SE-DL)generalization model effectively classifies the water quality index(WQI)based on 23 input variables.The proposed algorithm achieved a remarkable average of 95.69%,94.96%,92.92%,and 93.88%for accuracy,precision,recall,and F1 score,respectively.In addition,the proposed model is compared against two state-of-the-art classifiers,the XGBoost(eXtreme Gradient Boosting)and Light Gradient Boosting Machine,where performance metrics of balanced accuracy and g-mean are included.The experimental setup concluded XGBoost with a higher balanced accuracy and G-mean.However,the SE-DL model has a better and more balanced performance in the F1 score.The SE-DL model aligns with the goal of this study to ensure the balance between accuracy and completeness for each water quality class.The proposed algorithm is also capable of higher efficiency at a lower computational time against using the standard SyntheticMinority Oversampling Technique(SMOTE)approach to imbalanced datasets.

GraphCWGAN-GP:A Novel Data Augmenting Approach for Imbalanced Encrypted Traffic Classification

Encrypted traffic classification has become a hot issue in network security research.The class imbalance problem of traffic samples often causes the deterioration of Machine Learning based classifier performance.Although the Generative Adversarial Network(GAN)method can generate new samples by learning the feature distribution of the original samples,it is confronted with the problems of unstable training andmode collapse.To this end,a novel data augmenting approach called Graph CWGAN-GP is proposed in this paper.The traffic data is first converted into grayscale images as the input for the proposed model.Then,the minority class data is augmented with our proposed model,which is built by introducing conditional constraints and a new distance metric in typical GAN.Finally,the classical deep learning model is adopted as a classifier to classify datasets augmented by the Condition GAN(CGAN),Wasserstein GAN-Gradient Penalty(WGAN-GP)and Graph CWGAN-GP,respectively.Compared with the state-of-the-art GAN methods,the Graph CWGAN-GP cannot only control the modes of the data to be generated,but also overcome the problem of unstable training and generate more realistic and diverse samples.The experimental results show that the classification precision,recall and F1-Score of theminority class in the balanced dataset augmented in this paper have improved by more than 2.37%,3.39% and 4.57%,respectively.

An Imbalanced Dataset and Class Overlapping Classification Model for Big Data

Most modern technologies,such as social media,smart cities,and the internet of things(IoT),rely on big data.When big data is used in the real-world applications,two data challenges such as class overlap and class imbalance arises.When dealing with large datasets,most traditional classifiers are stuck in the local optimum problem.As a result,it’s necessary to look into new methods for dealing with large data collections.Several solutions have been proposed for overcoming this issue.The rapid growth of the available data threatens to limit the usefulness of many traditional methods.Methods such as oversampling and undersampling have shown great promises in addressing the issues of class imbalance.Among all of these techniques,Synthetic Minority Oversampling TechniquE(SMOTE)has produced the best results by generating synthetic samples for the minority class in creating a balanced dataset.The issue is that their practical applicability is restricted to problems involving tens of thousands or lower instances of each.In this paper,we have proposed a parallel mode method using SMOTE and MapReduce strategy,this distributes the operation of the algorithm among a group of computational nodes for addressing the aforementioned problem.Our proposed solution has been divided into three stages.Thefirst stage involves the process of splitting the data into different blocks using a mapping function,followed by a pre-processing step for each mapping block that employs a hybrid SMOTE algo-rithm for solving the class imbalanced problem.On each map block,a decision tree model would be constructed.Finally,the decision tree blocks would be com-bined for creating a classification model.We have used numerous datasets with up to 4 million instances in our experiments for testing the proposed scheme’s cap-abilities.As a result,the Hybrid SMOTE appears to have good scalability within the framework proposed,and it also cuts down the processing time.

Imbalanced Data Classification Using SVM Based on Improved Simulated Annealing Featuring Synthetic Data Generation and Reduction

Imbalanced data classification is one of the major problems in machine learning.This imbalanced dataset typically has significant differences in the number of data samples between its classes.In most cases,the performance of the machine learning algorithm such as Support Vector Machine(SVM)is affected when dealing with an imbalanced dataset.The classification accuracy is mostly skewed toward the majority class and poor results are exhibited in the prediction of minority-class samples.In this paper,a hybrid approach combining data pre-processing technique andSVMalgorithm based on improved Simulated Annealing(SA)was proposed.Firstly,the data preprocessing technique which primarily aims at solving the resampling strategy of handling imbalanced datasets was proposed.In 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 data.Next is the training of a balanced dataset using SVM.Since this algorithm requires an iterative process to search for the best penalty parameter during training,an improved SA algorithm was proposed for this task.In 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 process.Experimental 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 SVM.Registering at an average of 89.65%of accuracy for the binary class classification has demonstrated the good performance of the proposed works.

Observation points classifier ensemble for high-dimensional imbalanced classification

In this paper,an Observation Points Classifier Ensemble(OPCE)algorithm is proposed to deal with High-Dimensional Imbalanced Classification(HDIC)problems based on data processed using the Multi-Dimensional Scaling(MDS)feature extraction technique.First,dimensionality of the original imbalanced data is reduced using MDS so that distances between any two different samples are preserved as well as possible.Second,a novel OPCE algorithm is applied to classify imbalanced samples by placing optimised observation points in a low-dimensional data space.Third,optimization of the observation point mappings is carried out to obtain a reliable assessment of the unknown samples.Exhaustive experiments have been conducted to evaluate the feasibility,rationality,and effectiveness of the proposed OPCE algorithm using seven benchmark HDIC data sets.Experimental results show that(1)the OPCE algorithm can be trained faster on low-dimensional imbalanced data than on high-dimensional data;(2)the OPCE algorithm can correctly identify samples as the number of optimised observation points is increased;and(3)statistical analysis reveals that OPCE yields better HDIC performances on the selected data sets in comparison with eight other HDIC algorithms.This demonstrates that OPCE is a viable algorithm to deal with HDIC problems.

Machine Learning and Synthetic Minority Oversampling Techniques for Imbalanced Data: Improving Machine Failure Prediction

Prediction of machine failure is challenging as the dataset is often imbalanced with a low failure rate.The 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)algorithms.This 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 industry.The 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 indicator.The accuracy of the classifiers is very high and close to 100%,but the sensitivity of all classifiers using the original dataset was close to zero.The 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 F-measure.Results showed that the sensitivity of all classifiers increases as the imbalance rate increases.SVM 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.

Fault Diagnosis of Power Transformer Based on Improved ACGAN Under Imbalanced Data

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 learning.To 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 results.The 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 diagnosis.The 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 large.The 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 methods.Therefore,the method presented in this study has excellent and reliable fault diagnosis performance for various unbalanced datasets.In addition,the proposed approach can also solve the problems of insufficient and imbalanced fault data in other practical application fields.

An improved bidirectional generative adversarial network model for multivariate estimation of correlated and imbalanced tunnel construction parameters

Estimation of construction parameters is crucial for optimizing tunnel construction schedule.Due to the influence of routine activities and occasional risk events,these parameters are usually correlated and imbalanced.To solve this issue,an improved bidirectional generative adversarial network(BiGAN)model with a joint discriminator structure and zero-centered gradient penalty(0-GP)is proposed.In this model,in order to improve the capability of original BiGAN in learning imbalanced parameters,the joint discriminator separately discriminates the routine activities and risk event durations to balance their influence weights.Then,the self-attention mechanism is embedded so that the discriminator can pay more attention to the imbalanced parameters.Finally,the 0-GP is adapted for the loss of the discrimi-nator to improve its convergence and stability.A case study of a tunnel in China shows that the improved BiGAN can obtain parameter estimates consistent with the classical Gauss mixture model,without the need of tedious and complex correlation analysis.The proposed joint discriminator can increase the ability of BiGAN in estimating imbalanced construction parameters,and the 0-GP can ensure the stability and convergence of the model.

An Embedded Feature Selection Method for Imbalanced Data Classification

Imbalanced data is one type of datasets that are frequently found in real-world applications, e.g., fraud detection and cancer diagnosis. For this type of datasets, improving the accuracy to identify their minority class is a critically important issue.Feature selection is one method to address this issue. An effective feature selection method can choose a subset of features that favor in the accurate determination of the minority class. A decision tree is a classifier that can be built up by using different splitting criteria. Its advantage is the ease of detecting which feature is used as a splitting node. Thus, it is possible to use a decision tree splitting criterion as a feature selection method. In this paper, an embedded feature selection method using our proposed weighted Gini index(WGI) is proposed. Its comparison results with Chi2, F-statistic and Gini index feature selection methods show that F-statistic and Chi2 reach the best performance when only a few features are selected. As the number of selected features increases, our proposed method has the highest probability of achieving the best performance. The area under a receiver operating characteristic curve(ROC AUC) and F-measure are used as evaluation criteria. Experimental results with two datasets show that ROC AUC performance can be high, even if only a few features are selected and used, and only changes slightly as more and more features are selected. However, the performance of Fmeasure achieves excellent performance only if 20% or more of features are chosen. The results are helpful for practitioners to select a proper feature selection method when facing a practical problem.

Over-sampling algorithm for imbalanced data classification

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 borderline.We 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.

Using Imbalanced Triangle Synthetic Data for Machine Learning Anomaly Detection

The extreme imbalanced data problem is the core issue in anomaly detection.The amount of abnormal data is so small that we cannot get adequate information to analyze it.The mainstream methods focus on taking fully advantages of the normal data,of which the discrimination method is that the data not belonging to normal data distribution is the anomaly.From the view of data science,we concentrate on the abnormal data and generate artificial abnormal samples by machine learning method.In this kind of technologies,Synthetic Minority Over-sampling Technique and its improved algorithms are representative milestones,which generate synthetic examples randomly in selected line segments.In our work,we break the limitation of line segment and propose an Imbalanced Triangle Synthetic Data method.In theory,our method covers a wider range.In experiment with real world data,our method performs better than the SMOTE and its meliorations.

A Rebalancing Framework for Classification of Imbalanced Medical Appointment No-show Data

Purpose: This paper aims to improve the classification performance when the data is imbalanced by applying different sampling techniques available in Machine Learning.Design/methodology/approach: The medical appointment no-show dataset is imbalanced, and when classification algorithms are applied directly to the dataset, it is biased towards the majority class, ignoring the minority class. To avoid this issue, multiple sampling techniques such as Random Over Sampling(ROS), Random Under Sampling(RUS), Synthetic Minority Oversampling TEchnique(SMOTE), ADAptive SYNthetic Sampling(ADASYN), Edited Nearest Neighbor(ENN), and Condensed Nearest Neighbor(CNN) are applied in order to make the dataset balanced. The performance is assessed by the Decision Tree classifier with the listed sampling techniques and the best performance is identified.Findings: This study focuses on the comparison of the performance metrics of various sampling methods widely used. It is revealed that, compared to other techniques, the Recall is high when ENN is applied CNN and ADASYN have performed equally well on the Imbalanced data.Research limitations: The testing was carried out with limited dataset and needs to be tested with a larger dataset.Practical implications: This framework will be useful whenever the data is imbalanced in real world scenarios, which ultimately improves the performance.Originality/value: This paper uses the rebalancing framework on medical appointment no-show dataset to predict the no-shows and removes the bias towards minority class.

Dealing with Imbalanced Dataset Leveraging Boundary Samples Discovered by Support Vector Data Description

These days,imbalanced datasets,denoted throughout the paper by ID,(a dataset that contains some(usually two)classes where one contains considerably smaller number of samples than the other(s))emerge in many real world problems(like health care systems or disease diagnosis systems,anomaly detection,fraud detection,stream based malware detection systems,and so on)and these datasets cause some problems(like under-training of minority class(es)and over-training of majority class(es),bias towards majority class(es),and so on)in classification process and application.Therefore,these datasets take the focus of many researchers in any science and there are several solutions for dealing with this problem.The main aim of this study for dealing with IDs is to resample the borderline samples discovered by Support Vector Data Description(SVDD).There are naturally two kinds of resampling:Under-sampling(U-S)and oversampling(O-S).The O-S may cause the occurrence of over-fitting(the occurrence of over-fitting is its main drawback).The U-S can cause the occurrence of significant information loss(the occurrence of significant information loss is its main drawback).In this study,to avoid the drawbacks of the sampling techniques,we focus on the samples that may be misclassified.The data points that can be misclassified are considered to be the borderline data points which are on border(s)between the majority class(es)and minority class(es).First by SVDD,we find the borderline examples;then,the data resampling is applied over them.At the next step,the base classifier is trained on the newly created dataset.Finally,we compare the result of our method in terms of Area Under Curve(AUC)and F-measure and G-mean with the other state-of-the-art methods.We show that our method has betterresults than the other state-of-the-art methods on our experimental study.

Imbalanced Classification in Diabetics Using Ensembled Machine Learning

Diabetics is one of the world’s most common diseases which are caused by continued high levels of blood sugar.The risk of diabetics can be lowered if the diabetic is found at the early stage.In recent days,several machine learning models were developed to predict the diabetic presence at an early stage.In this paper,we propose an embedded-based machine learning model that combines the split-vote method and instance duplication to leverage an imbalanced dataset called PIMA Indian to increase the prediction of diabetics.The proposed method uses both the concept of over-sampling and under-sampling along with model weighting to increase the performance of classification.Different measures such as Accuracy,Precision,Recall,and F1-Score are used to evaluate the model.The results we obtained using K-Nearest Neighbor(kNN),Naïve Bayes(NB),Support Vector Machines(SVM),Random Forest(RF),Logistic Regression(LR),and Decision Trees(DT)were 89.32%,91.44%,95.78%,89.3%,81.76%,and 80.38%respectively.The SVM model is more efficient than other models which are 21.38%more than exiting machine learning-based works.

A Rasterized Lightning Disaster Risk Method for Imbalanced Sets Using Neural

Over the past 10 years,lightning disaster has caused a large number of casualties and considerable economic loss worldwide.Lightning poses a huge threat to various industries.In an attempt to reduce the risk of lightning-caused disaster,many scholars have carried out in-depth research on lightning.However,these studies focus primarily on the lightning itself and other meteorological elements are ignored.In addition,the methods for assessing the risk of lightning disaster fail to give detailed attention to regional features(lightning disaster risk).This paper proposes a grid-based risk assessment method based on data from multiple sources.First,this paper considers the impact of lightning,the population density,the economy,and geographical environment data on the occurrence of lightning disasters;Second,this paper solves the problem of imbalanced lightning disaster data in geographic grid samples based on the K-means clustering algorithm;Third,the method calculates the feature of lightning disaster in each small field with the help of neural network structure,and the calculation results are then visually reflected in a zoning map by the Jenks natural breaks algorithm.The experimental results show that our method can solve the problem of imbalanced lightning disaster data,and offer 81%accuracy in lightning disaster risk assessment.

An Effective Classifier Model for Imbalanced Network Attack Data

Recently,machine learning algorithms have been used in the detection and classification of network attacks.The performance of the algorithms has been evaluated by using benchmark network intrusion datasets such as DARPA98,KDD’99,NSL-KDD,UNSW-NB15,and Caida DDoS.However,these datasets have two major challenges:imbalanced data and highdimensional data.Obtaining high accuracy for all attack types in the dataset allows for high accuracy in imbalanced datasets.On the other hand,having a large number of features increases the runtime load on the algorithms.A novel model is proposed in this paper to overcome these two concerns.The number of features in the model,which has been tested at CICIDS2017,is initially optimized by using genetic algorithms.This optimum feature set has been used to classify network attacks with six well-known classifiers according to high f1-score and g-mean value in minimumtime.Afterwards,amulti-layer perceptron based ensemble learning approach has been applied to improve the models’overall performance.The experimental results showthat the suggested model is acceptable for feature selection as well as classifying network attacks in an imbalanced dataset,with a high f1-score(0.91)and g-mean(0.99)value.Furthermore,it has outperformed base classifier models and voting procedures.