Fuzzy Support Vector Machine Research Articles

Divisional intuitionistic fuzzy least squares twin SVM for pipeline leakage detection

Improving the accuracy of pipeline leak detection in noisy environments is significant for maintaining pipeline safety. Currently, the weighted support vector machine (SVM) is extensively employed. Among them, Intuitionistic fuzzy twin SVM (IFTSVM) has good robustness due to the integration of intuitionistic fuzzy set theory. However, IFTSVM does not fully consider the position information of samples in the feature space, cannot reduce the impact of heterogeneous clustering noise. Moreover, it requires solving two quadratic programming problems (QPPs), which is inefficient. To address the aforementioned issues, this paper proposes a divisional intuitionistic fuzzy least squares twin SVM (DIFL-TSVM) for pipeline leakage detection. This approach proposes a novel divisional intuitionistic fuzzy membership calculation strategy to reduce the impact of heterogeneous clustering noise and simplify the calculation of sample membership. In addition, DIFL-TSVM transforms the original QPPs into two linear equations using the least squares method to improve the efficiency of model. This paper verifies that the DIFL-TSVM model not only has the ability to eliminate the effects of discrete noise and heterogeneous clustering noise but also more efficiency on water supply pipeline leakage diagnosis experiments and UCI datasets. The experimental results show that DIFL-TSVM has higher leak identification with a detection accuracy of 95.6 %.

Toward effective SVM sample reduction based on fuzzy membership functions

Support vector machine (SVM) is known for its good generalization performance and wide application in various fields. Despite its success, the learning efficiency of SVM decreases significantly originating from the assumption that the number of training samples increases rapidly. Consequently, the traditional SVM with standard optimization methods faces challenges such as excessive memory requirements and slow training speed, especially for large-scale training sets. To address this issue, this paper draws inspiration from the fuzzy support vector machine (FSVM). Considering that each sample has varying contributions to the decision plane, we propose an effective SVM sample reduction method based on the fuzzy membership function (FMF). This method uses FMF to calculate the fuzzy membership of each training sample. Training samples with low fuzzy memberships are then deleted. Specifically, we propose SVM sample reduction algorithms based on class center distance, kernel target alignment, centered kernel alignment, slack factor, entropy, and bilateral weighted FMF, respectively. Comprehensive experiments on UCI and KEEL datasets demonstrate that our proposed algorithms outperform other comparative methods in terms of accuracy, F-measure, and hinge-loss measures.

Fuzzy support vector machine using local outlier factor and intuitionistic fuzzy sets for imbalanced datasets

Traditional classifiers are commonly used for solving class balance problems. However, many datasets exhibit class imbalance along with outliers and noise, which affect the classification accuracy, this paper proposes a fuzzy support vector machine algorithm based on local outlier factor and intuitionistic fuzzy sets. First, to highlight the importance of the minority class, the membership degree is set to the maximum value. Meanwhile, local outlier factor is calculated to measure the abnormality and further obtain the membership degree in the majority class. Then, a kernel density estimation method is designed to estimate the sample density accurately. Furthermore, based on the density distribution, intuitionistic fuzzy sets are used to assign membership and non-membership degrees to samples, effectively distinguishing between noise and outliers. Finally, different penalty coefficients for two classes are built to offset the impact of class imbalance on classification accuracy. Experimental results show the advantages of the proposed algorithm.

Bell-Shaped Fuzzy Least Square Twin SVM With Biomedical Applications

Bell-Shaped Fuzzy Least Square Twin SVM With Biomedical Applications

Intuitionistic fuzzy twin proximal SVM with fuzzy hyperplane and its application in EEG signal classification

Twin support vector machine (TSVM) is a contemporary machine learning technique to tackle classification and regression problems. However, TSVM lacks the ability to differentiate between support vectors and noises since it neglects the positional information of input data samples, and hence it is sensitive to noises. Additionally, it fails to consider uncertainties associated with the data, which reduces its capacity for generalization. To address these drawbacks, we propose a novel fuzzy hyperplane based intuitionistic fuzzy twin proximal support vector machine. The first significant feature of the proposed approach is that it gives an intuitionistic fuzzy number based on the relevance to each data vector. This efficiently reduces the impact of noise and outliers by leveraging the local neighborhood information within the data points by incorporating membership and non-membership weights. Secondly, all the parameters present in the model are fuzzy variables, including the offset term and the elements of the normal vector. The suggested fuzzy hyperplane successfully reflects the inherent ambiguity prevalent in real-world categorization problems by reflecting vagueness in the input data through the use of fuzzy variables. The model’s efficiency is enhanced by solving two systems of linear equations to obtain two non-parallel classifiers rather than solving two quadratic programming problems as in standard TSVM. Utilizing non-linear kernel functions within the feature space enables the method to effectively identify complex patterns or non-linear relationships within the datasets. In order to demonstrate the effectiveness of the suggested approach, extensive computer experiments have been conducted on a set of eighteen benchmark datasets with both linear and non-linear kernels. In addition, rigorous statistical analysis, including Friedman and post-hoc Nemenyi tests, have been employed to assess the significance of the observed performance differences. Furthermore, we also performed numerical experiments utilizing linear, Gaussian, and polynomial kernels to classify electroencephalogram (EEG) signals. The outcomes of the experiment are analyzed in terms of average accuracy, processing time, and F-measure. The results demonstrate that the proposed method outperforms existing methods and achieves better generalization.

Cervical Cancer Segmentation using Fuzzy Support Vector Machine Algorithm

Cervical cancer is a dangerous disease, particularly prevalent in developing countries where public awareness is low. The Papanicolaou test, commonly known as the Pap test, is the most widely used method to detect cervical cancer, which develops in the cervix and affects many women. Image processing algorithms play an important role in the segmentation of the cancerous region in cervical images. The fuzzy support vector machine (FSVM) algorithm is used to segment the cancerous regions in cervical cancer images. This method effectively separates the cervical cancer regions from the background in these images. The K-means classification algorithm is another existing method applied to cervical cancer images. The results of the existing and proposed segmentation algorithms are compared using quality measurement techniques such as accuracy and precision. The proposed FSVM algorithm demonstrated the highest accuracy (98%) compared to the previous algorithms.

Maize leaf disease recognition using PRF-SVM integration: a breakthrough technique

The difficulty of collecting maize leaf lesion characteristics in an environment that undergoes frequent changes, suffers varying illumination from lighting sources, and is influenced by a variety of other factors makes detecting diseases in maize leaves difficult. It is critical to monitor and identify plant leaf diseases during the initial growing period to take suitable preventative measures. In this work, we propose an automated maize leaf disease recognition system constructed using the PRF-SVM model. The PRFSVM model was constructed by combining three powerful components: PSPNet, ResNet50, and Fuzzy Support Vector Machine (Fuzzy SVM). The combination of PSPNet and ResNet50 not only assures that the model can capture delicate visual features but also allows for end-to-end training for smooth integration. Fuzzy SVM is included as a final classification layer to accommodate the inherent fuzziness and uncertainty in real-world image data. Five different maize crop diseases (common rust, southern rust, grey leaf spot, maydis leaf blight, and turcicum leaf blight along with healthy leaves) are selected from the Plant Village dataset for the algorithm’s evaluation. The average accuracy achieved using the proposed method is approximately 96.67%. The PRFSVM model achieves an average accuracy rating of 96.67% and a mAP value of 0.81, demonstrating the efficacy of our approach for detecting and classifying various forms of maize leaf diseases.

Fuzzy SVM With Mahalanobis Distance for Situational Awareness-Based Recognition of Public Health Emergencies

In public health emergencies, situational awareness is crucial for swift responses by governments and rescue organizations. In this manuscript, a novel framework is proposed to identify and classify event-specific information, aiming to comprehend concepts, characteristics, and classifications associated with situational awareness in social media emergencies. First, a statistical approach is employed to extract a set of standard features. Second, a category-based latent dirichlet allocation to vector (LDA2vec) model is leveraged to extract topic-based features to enhance accuracy, particularly for unbalanced datasets. Finally, a fuzzy support vector machine (FSVM) classifier utilizing the Mahalanobis distance kernel is introduced to improve the detection accuracy of event-specific information. The framework's effectiveness is evaluated using the social media public health dataset, achieving superior filtering capabilities for non-informative data with a precision of 89% and an F1-Score of 91%, surpassing other standard methods.

Application of Sports Video Image Analysis Based on Fuzzy Support Vector Machine

Sports video image has always been a hot topic in sports video processing. The theoretical and experimental analysis of digital image noise reduction technology is a challenging topic. In this paper, a sports video denoising algorithm is designed by combining the excellent characteristics of curvilinear transformation theory and fuzzy support vector machine. Firstly, the image with noise is curvilinear, and the conversion coefficient is obtained. Then, according to the distribution characteristics of the system noise, the system parameters are divided into space, and the system learning features are constructed. The fuzzy classification of high-frequency curves is realized using the adaptive threshold denoising method. Then, the noise reduction coefficient is reconstructed by the curve-wave method to obtain the processed image. The simulation results show that this method can overcome the pseudo-Gibbs effect effectively and suppress the noise well. This algorithm has a good application prospect in sports video image processing.

Research on long term power load grey combination forecasting based on fuzzy support vector machine

Aiming at the problem of low accuracy of single model in power load forecasting, a method based on fuzzy support vector machine and grey combination forecasting is proposed. The power load data is analyzed and prepared by means of data repair, preprocessing and fuzzy C-means clustering, which is combined with fuzzy support vector machine and grey prediction. The clustering results are taken as the input of the grey prediction model, and the preliminary prediction results are obtained through parameter modification. Then, the results are taken as the input of support vector machine, and the long-term power load prediction is carried out by combining the two models. The experimental results show that the proposed method has excellent performance in predicting long-term power load accurately, and the prediction accuracy is significantly improved compared with the traditional single forecasting model. The application of this method to long-term power load forecasting below 4000 KWH proves its high prediction accuracy and reliability once again. When the clustering category of power load fuzzy clustering is set to 30, the best data clustering effect can be obtained, and the prediction accuracy is further improved.

An Improved Model Using Oversampling Technique and Cost-Sensitive Learning for Imbalanced Data Problem

In today's world, classification learning is a vital task because of the advancement in technology. However, during the classification process, we found the classifiers (the traditional classification techniques) couldn't handle the imbalanced data, which means the instances (majority instances) that belong to one class are many more than the instances (minority instances) that belong to another class. The use of oversampling approaches and cost-sensitive strategies are two popular approaches for addressing the imbalanced class snag. However, the best outcomes are achieved by combining the two approaches. So, the paper's concentration is to propose an enhancement model by combining the cost-sensitive technique adapted from the entropy-based fuzzy support vector machine algorithm (EFSVM), called entropy-based fuzzy membership, and the oversampling method, and provide a comparison among imbalanced learning techniques on KEEL and UCI repositories. According to the experimental findings, our enhanced model will outperform all existing models in terms of performance.

Curriculum Learning-Based Fuzzy Support Vector Machine

Curriculum Learning-Based Fuzzy Support Vector Machine

Research on phosphorite photoelectric sorting process based on fuzzy algorithm

Abstract In this paper, we synthesize the differences in grayscale and texture information of phosphorite X-ray images and extract a total of six feature quantities under their grayscale and texture features, respectively. After feature fusion, a fuzzy support vector machine classifier with a normal plane-type affiliation function is used to optimize the binary classification method of traditional support vector machines, which improves the accuracy of classification. To improve the sorting model’s division of the optimal classification hyperplane, the centroid position of the two types of samples is considered comprehensively. In order to give full play to the classification performance of the sorting model, the particle swarm algorithm is further used to optimize the core parameters of the fuzzy support vector machine, the penalty factor, and the kernel parameters to complete the construction of the classifier model. In the face of the mixed colloidal phosphorite that can not be exploited, based on the PSO-NP-FSVM photoelectric sorting operation, the concentrate yield rate, operation recovery rate, and optical sorting waste rate are 56.52%, 20.87%, and 71.17%, respectively. After photoelectric sorting, the grade of concentrate P2 O5 can be increased to 20.98%, which can meet the requirements for the grade of the processing plant. Therefore, the photoelectric sorting process combined with a fuzzy algorithm is capable of meeting the actual production requirements.

Peak carbon emission prediction of tourist attractions based on fuzzy support vector machine

Peak carbon emission prediction of tourist attractions based on fuzzy support vector machine

A Fourth‐Order Fuzzy Support Vector Machine Based on Class Center

A Fourth‐Order Fuzzy Support Vector Machine Based on Class Center

Forecasting Analysis of Customer Subscribed Time Deposits Based on Pinball Loss Fuzzy Support Vector Machines

Forecasting Analysis of Customer Subscribed Time Deposits Based on Pinball Loss Fuzzy Support Vector Machines

Analyzing Multi-Mode Fatigue Information from Speech and Gaze Data from Air Traffic Controllers

In order to determine the fatigue state of air traffic controllers from air talk, an algorithm is proposed for discriminating the fatigue state of controllers based on applying multi-speech feature fusion to voice data using a Fuzzy Support Vector Machine (FSVM). To supplement the basis for discrimination, we also extracted eye-fatigue-state discrimination features based on Percentage of Eyelid Closure Duration (PERCLOS) eye data. To merge the two classes of discrimination results, a new controller fatigue-state evaluation index based on the entropy weight method is proposed, based on a decision-level fusion of fatigue discrimination results for speech and the eyes. The experimental results show that the fatigue-state recognition accuracy rate was 86.0% for the fatigue state evaluation index, which was 3.5% and 2.2%higher than those for speech and eye assessments, respectively. The comprehensive fatigue evaluation index provides important reference values for controller scheduling and mental-state evaluations.

Fuzzy Support Vector Machine Based Outlier Detection for Financial Credit Score Prediction System

Economic Credit Scoring (CS), which aids in calculating the credit worth of both individuals and companies, is regarded as one of the greatest study issues in the finance field. In the banking industry, data mining techniques are believed to be helpful since they help designers and developers create appropriate goods or services for customers with the fewest possible risks. Losses and loan cancellations, which are the major sources of hazards in the banking industry, are related to credit risks. A Support Vector Machine based architecture is presented in the current study for the financial credit score prediction system. However, the existing work tends to have increased computational overhead and that requires complete data for attaining required accurate rate. The system known as the Fuzzy Support Vector Machine based Outlier Detection System is introduced in the suggested research study to address this (FSVM-ODS). This study's first grouping of data items utilising a hybrid genetic algorithm with K-Means clustering algorithm is named (HKGA). The dataset must be gradually lowered in size, and calculation time must likewise be decreased. The Enhanced Z-score (EZS) outlier identification (OD) technique was employed in the second step to identify outliers in the dataset. Then, we use a customized beaver searching method to choose the database. For categorization of the datasets, a fuzzy support vector machine is utilised. The whole study project is carried out in the Matlab simulation environment, and it has been shown that the suggested technique achieves a higher outlier identification rate than the current methodology.

PREDICTING 10 YEAR MORTALITY WITH MACHINE LEARNING: EVIDENCE FROM NATIONAL SOCIAL LIFE, HEALTH, AND AGING PROJECT

Abstract The current academic literature extensively used linear logistic models with social, behavioral, and psychological status to predict mortality. However, few address the interdependency of predictors and the imbalance of targets that adversely bias the results. Using the National Social Life Health and Aging Project (NSHAP), we developed two machine learning models predicting the 10-year mortality of older adults in the US. We first used tree-based algorithms of Decision Tree (DT) that account for the interdependency of the social features and decide the splitting nodes and thresholds using entropy gain conditional on the previous splitting predictor to discern disposition status. Second, we used the Fuzzy Support Vector Machine (FSVM) that regards every sample as a node in high-dimensional vector space and splits the nodes with an optimum plane by finding the best linear combination of features to get an optimum prediction accuracy. Additionally, FSVM addresses the target imbalance problem by conducting a more delicate classification of samples with close predicted probabilities of being alive and deceased. Compared to the accuracy rates achieved by the Logistic Regression, our algorithms perform better on the entire population and the population near the class boundary. We also discussed the social and demographic characteristics of the cases whose disposition statuses were either wrongly predicted as deceased or alive by our algorithms. The findings serve important purposes for public health practitioners in accurately understanding the risk and protective factors of mortality in aging.

Three-way imbalanced learning based on fuzzy twin SVM

Three-way decision (3WD) is a powerful tool for granular computing to deal with uncertain data, commonly used in information systems, decision-making, and medical care. Three-way decision gets much research in traditional rough set models. However, three-way decision is rarely combined with the currently popular field of machine learning to expand its research. In this paper, three-way decision is connected with SVM, a standard binary classification model in machine learning, for solving imbalanced classification problems that SVM needs to improve. A new three-way fuzzy membership function and a new fuzzy twin support vector machine with three-way membership (TWFTSVM) are proposed. The new three-way fuzzy membership function is defined to increase the certainty of uncertain data in both input space and feature space, which assigns higher fuzzy membership to minority samples compared with majority samples. To evaluate the effectiveness of the proposed model, comparative experiments are designed for forty-seven different datasets with varying imbalance ratios. In addition, datasets with different imbalance ratios are derived from the same dataset to further assess the proposed model’s performance. The results show that the proposed model significantly outperforms other traditional SVM-based methods.