Fuzzy Support Vector Machine Model Research Articles

Construction and application of computerized risk assessment model for supply chain finance under technology empowerment.

This study seeks to assist small and medium enterprises break free of the constraints of the conventional financing model and lessen the supply chain finance risks they face. First, the supply chain financial business model and credit risk are analyzed, followed by a discussion of the application principle of blockchain in the control of supply chain financial credit risk. The next topic up for discussion is the emancipation of individuals and the application of financial technology toward the management of financial risk in supply chains. In the final stage of the development of the computerized risk assessment model, the Fuzzy Support Vector Machine (FSVM) is optimized, and the effectiveness and efficiency of risk classification are enhanced by introducing a variable penalty factor C. To test the efficacy of the C-FSVM risk assessment model, the Chinese auto sector is used as the study's object. According to the results of the study, the C-FSVM model has a classification accuracy of 96.35% for the entire sample, 96.45% for credible firms, and 95.34% for default enterprises. The training time of the C-FSVM model is 473.9s, which is far lower than the SVM and FSVM models' training times of 1631.6s and 1870.2s. In summary, the C-FSVM supply chain financial risk assessment model is effective and has great application value in banking practice.

Innovative Evaluation Method of Government Physical Education Training Quality Based on Fuzzy Mathematics

Current sports training quality evaluation methods cannot accurately detect the integrity of training data, resulting in low accuracy of training quality evaluation. Considering the function of the government in providing public sports services and the role of the government in coordinating the sports training of all parties, this paper puts forward an innovative evaluation method of government physical education training quality based on fuzzy mathematics. First of all, the real-time data on sports training sensor signal is obtained by using SiCN (Silicon Carbonitride) sensor, and the real-time detection circuit and data amplification module are designed. Based on the core hardware for instant data detection of sports training sensor signal, a fuzzy support vector machine model is established to detect the integrity of government sports innovation training data, reconstruct the track of sports training action, and thus construct an innovative evaluation model of government sports training quality based on heredity and support vector machine. Experimental results show that compared with existing methods, the proposed method has higher accuracy in data integrity detection, better reconstruction effect in sports training action, with evaluation results reaching more than 90 points.

Multi-objective Metaheuristics with Intelligent Deep Learning Model for Pancreatic Tumor Diagnosis

Pancreatic tumor is the deadliest disease which needs earlier identification to reduce the mortality rate. With this motivation, this study introduces a Multi-Objective Metaheuristics with Intelligent Deep Learning Model for Pancreatic Tumor Diagnosis (MOM-IDL) model. The proposed MOM-IDL technique encompasses an adaptive Weiner filter based pre-processing technique to enhance the image quality and get rid of the noise. In addition, multi-level thresholding based segmentation using Kapur’s entropy is employed where the threshold values are optimally chosen by the barnacles mating optimizer (BMO). Besides, densely connected network (DenseNet-169) is employed as a feature extractor and fuzzy support vector machine (FSVM) is utilized as a classifier. For improving the classification performance, the BMO technique was implemented for fine-tuning the parameters of the FSVM model. The design of MOBMO algorithm for threshold selection and parameter optimization processes shows the novelty of the work. A wide range of simulations take place on the benchmark dataset and the experimental results highlighted the enhanced performance of the MOM-IDL technique over the recent state of art techniques.

Intrusion Detection System for Energy Efficient Cluster Based Vehicular Adhoc Networks

A vehicular ad hoc network (VANET), a subfield of mobile adhoc network (MANET) is defined by its high mobility by demonstrating the dissimilar mobility patterns. So, VANET clustering techniques are needed with the consideration of the mobility parameters amongst the nearby nodes for constructing the stable clustering techniques. At the same time, security is also a major design issue in VANET, this can be resolved by the intrusion detection systems (IDS). In contrast to the conventional IDS, VANET based IDS are required to be designed in such a way that the functioning of the system does not affect the real-time efficiency of the performance of VANET applications. With this motivation, this paper presents an efficient Fuzzy Logic based Clustering with optimal fuzzy support vector machine (FSVM), called FLC-OFSVM based on the Intrusion Detection System for VANET. The proposed FLC-OFSVM model involves two stages of operations namely clustering and intrusion detections. Primarily, FLC technique is employed for selecting an appropriate set of cluster heads (CHs) and for constructing the clusters. Besides, a lightweight anomaly IDS model named FSVM optimized with krill herd (KH) optimization algorithm is developed for detecting the existence of malevolent attacks in VANET. The KH algorithm based on the herding behavior of krills is used for optimally tuning the parameters of the FSVM model. In order to investigate the performance of the FLC-OFSVM model, an extensive set of simulations have been carried out and the results thus showcased that the OFSVM model has gained maximum outcome with an accuracy of 99.98%.

Fuzzy Machine Learning Model in Real-World Physical Domains; A State-of-the-Art Approach

Day after day, individuals all across the world come up with fresh ideas for improving the future. Several intriguing discoveries and ideas paved the way for a new age of electronics, telecommunications, business, and medicinal innovation. Using less resources, greater changes in these domains can be achieved. As improving efficiency and productivity allow exponential development in some areas of the global economy, artificial intelligence (AI) and machine learning (ML) is being adopted by a growing number of individuals, corporations, and governments. Since real-world scenarios influence imprecise and unpredictable situations, fuzzy systems have become an inescapable machine learning aspect. Thus, this research presents a qualitative analysis of the significance of fuzzy machine learning systems like fuzzy support vector machines (FSVM) in various physical domains. Based on this analysis, this research extends with the proposal of a fuzzy machine learning-based framework for two different physical domains: (1) intelligent transportation and (2) ecological risk handling. Thereby, this state-of-the-art approach presents fault detection using FSVM (FD-FSVM) model in the intelligent transportation domain. In ecological risk handling, this study proposes an improved FSVM for risk level classification (FSVM-RLC) approach, which uses the persistent organic pollutants data for training and validation. These two domains are chosen randomly to evaluate the classification performance of the fuzzy machine learning algorithms based on their mean absolute error, accuracy, precision, recall, and F1 score. Apart from this, the mean square error and mean absolute error are measured. Compared to existing machine learning models, the individual results of these two approaches show the highest performance. Furthermore, this fuzzy integrated machine learning technique kept consistency in both domains by giving 98.2% and 97.89% accuracy levels for FD-FSVM and FSVM-RLC, respectively.

Optimization of Fuzzy Support Vector Machine (FSVM) Performance by Distance-Based Similarity Measure Classification

This research aims to determine the maximum or minimum value of a Fuzzy Support Vector Machine (FSVM) Algorithm using the optimization function. SVM is considered as an effective method of data classification, as opposed to FSVM, which is less effective on large and complex data because of its sensitivity to outliers and noise. One of the techniques used to overcome this inefficiency is fuzzy logic with its ability to select the right membership function, which significantly affects the effectiveness of the FSVM algorithm performance. This research was carried out using the Gaussian membership function and the Distance-Based Similarity Measurement consisting of the Euclidean, Manhattan, Chebyshev, and Minkowsky distance methods. Subsequently, the optimization of the FSVM classification process was determined using four proposed FSVM models and normal SVM as comparison references. The results showed that the method tends to eliminate the impact of noise and enhance classification accuracy effectively. FSVM provides the best and highest accuracy value of 94% at a penalty parameter value of 1000 using the Chebyshev distance matrix. Furthermore, the model proposed will be compared to the performance evaluation model in preliminary studies (Xiao Kang et al., 2018). The result further showed that using FSVM with Chebyshev distance matrix and a Gaussian membership function provides a better performance evaluation value. Doi: 10.28991/HIJ-2021-02-04-02 Full Text: PDF

Geometric Mean Maximum FSVMI Model and Its Application in Carotid Artery Stenosis Risk Prediction

Carotid artery stenosis is a serious medical condition that can lead to stroke. Using machine learning method to construct classifier model, carotid artery stenosis can be diagnosed with transcranial doppler data. We propose an improved fuzzy support vector machine model to predict carotid artery stenosis, with the maximum geometric mean as the optimization target. The fuzzy membership function is obtained by combining information entropy with the normalized class-center distance. Experimental results showed that the proposed model was superior to the benchmark models in sensitivity and geometric mean criteria.

A Self-Representation-Based Fuzzy SVM Model for Predicting Vascular Calcification of Hemodialysis Patients.

In end-stage renal disease (ESRD), vascular calcification risk factors are essential for the survival of hemodialysis patients. To effectively assess the level of vascular calcification, the machine learning algorithm can be used to predict the vascular calcification risk in ESRD patients. As the amount of collected data is unbalanced under different risk levels, it has an influence on the classification task. So, an effective fuzzy support vector machine based on self-representation (FSVM-SR) is proposed to predict vascular calcification risk in this work. In addition, our method is also compared with other conventional machine learning methods, and the results show that our method can better complete the classification task of the vascular calcification risk.

MK-FSVM-SVDD: A Multiple Kernel-based Fuzzy SVM Model for Predicting DNA-binding Proteins via Support Vector Data Description

MK-FSVM-SVDD: A Multiple Kernel-based Fuzzy SVM Model for Predicting DNA-binding Proteins via Support Vector Data Description

MK-FSVM-SVDD: A Multiple Kernel-based Fuzzy SVM Model for Predicting DNA-binding Proteins via Support Vector Data Description

Background: Detecting DNA-binding proteins (DBPs) based on biological and chemical methods is time-consuming and expensive. Objective: In recent years, the rise of computational biology methods based on Machine Learning (ML) has greatly improved the detection efficiency of DBPs. Method: In this study, the Multiple Kernel-based Fuzzy SVM Model with Support Vector Data Description (MK-FSVM-SVDD) is proposed to predict DBPs. Firstly, sex features are extracted from the protein sequence. Secondly, multiple kernels are constructed via these sequence features. Then, multiple kernels are integrated by Centered Kernel Alignment-based Multiple Kernel Learning (CKA-MKL). Next, fuzzy membership scores of training samples are calculated with Support Vector Data Description (SVDD). FSVM is trained and employed to detect new DBPs. Results: Our model is evaluated on several benchmark datasets. Compared with other methods, MKFSVM- SVDD achieves best Matthew's Correlation Coefficient (MCC) on PDB186 (0.7250) and PDB2272 (0.5476). Conclusion: We can conclude that MK-FSVM-SVDD is more suitable than common SVM, as the classifier for DNA-binding proteins identification.

An Optimization Model for Stock Market Direction Prediction

Stock market direction prediction becomes an essential task in the business sector. The inherent volatile behavior of stock markets worldwide makes the prediction process difficult. The improvement in the prediction accuracy of the stock market direction prediction helps to avoid the risks involved in the investment process. In this aspect, this study designs a swallow swarm optimization (SSO) with a fuzzy support vector machine (FSVM) model for stock market direction prediction. The proposed SSO-FSVM model encompasses preprocessing, feature extraction, FSVM, and SSO based parameter tuning. The usage of the SSO algorithm to fine-tune the parameters involved in the FSVM model helps to significantly improve the overall predictive performance. To validate the improved performance of the SSO-FSVM model, a wide range of experiments were carried out using two benchmark datasets. The experimental outcomes reported the betterment of the SSO-FSVM model over the recent approaches in terms of several evaluation metrics.

Financial Risk Early Warning Based on Wireless Network Communication and the Optimal Fuzzy SVM Artificial Intelligence Model

Since the beginning of the new century, risk events such as the world economic crisis have occurred, which have greatly impacted the real economy of our country. A wireless network is a network implemented using wireless communication technology. It includes both global voice and data networks that allow users to establish long‐distance wireless connections, as well as infrared technology and radio frequency technology optimized for short‐distance wireless connections. These events have a great impact on many small‐ and medium‐sized listed companies, resulting to many small‐ and medium‐sized listed companies going bankrupt. Indeed, one of the important reasons for the frequent bankruptcy of small‐ and medium‐sized listed companies is the lack of awareness of risk prevention and effective financial risk early warning mechanism. The support vector machine is a machine learning method based on the VC dimension theory of statistical learning and the principle of structural risk minimization. This method shows many unique advantages when dealing with classification problems and has been widely used in many fields. The purpose of this article is to realize the financial risk analysis of listed companies through wireless network communication and the optimal fuzzy SVM artificial intelligence model, which help small‐ and medium‐sized listed companies find abnormalities in their business management activities in advance and deal with market risks in a timely manner. Taking 81 small‐ and medium‐sized listed companies as the research object, this paper chooses the small‐ and medium‐sized listed companies in every quarter of 2018 as the research sample. By using the financial and nonfinancial data of small‐ and medium‐sized listed companies and introducing the support vector machine (SVM) with the fuzzy method, the model of the fuzzy support vector machine (FSVM) is constructed. And the performance of the FSVM under four different kernel functions is compared and studied. At the same time, the performance of the FSVM is compared with other artificial intelligence models. The empirical results show that different kernel functions have different effects on the prediction performance of the FCM‐SVM model. Under the Gauss radial basis function, the prediction accuracy of the FCM‐SVM is over 86%. It can be seen that in predicting the financial crisis of small‐ and medium‐sized listed companies, the FCM‐SVM model with Gauss radial basis function has the best predictive performance. The FSVM model based on Gauss radial basis function not only has the advantages of linearity, being polynomial, and nonlinearity of neurons but also is significantly superior to the traditional artificial intelligence model.

MK-FSVM-SVDD: A Multiple Kernel-based Fuzzy SVM Model for Predicting DNA-binding Proteins via Support Vector Data Description

MK-FSVM-SVDD: A Multiple Kernel-based Fuzzy SVM Model for Predicting DNA-binding Proteins via Support Vector Data Description

MK-FSVM-SVDD: A Multiple Kernel-based Fuzzy SVM Model for Predicting DNA-binding Proteins via Support Vector Data Description

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Identify Lysine Neddylation Sites Using Bi-profile Bayes Feature Extraction via the Chou's 5-steps Rule and General Pseudo Components.

Introduction Neddylation is a highly dynamic and reversible post-translational modification. The abnormality of neddylation has previously been shown to be closely related to some human diseases. The detection of neddylation sites is essential for elucidating the regulation mechanisms of protein neddylation.Objective As the detection of the lysine neddylation sites by the traditional experimental method is often expensive and time-consuming, it is imperative to design computational methods to identify neddylation sites.MethodsIn this study, a bioinformatics tool named NeddPred is developed to identify underlying protein neddylation sites. A bi-profile bayes feature extraction is used to encode neddylation sites and a fuzzy support vector machine model is utilized to overcome the problem of noise and class imbalance in the prediction.Results Matthew's correlation coefficient of NeddPred achieved 0.7082 and an area under the receiver operating characteristic curve of 0.9769. Independent tests show that NeddPred significantly outperforms existing lysine neddylation sites predictor NeddyPreddy.Conclusion Therefore, NeddPred can be a complement to the existing tools for the prediction of neddylation sites. A user-friendly webserver for NeddPred is accessible at 123.206.31.171/NeddPred/.

Centered kernel alignment inspired fuzzy support vector machine

Support vector machine (SVM) is a theoretically well motivated algorithm developed from statistical learning theory which has shown impressive performance in many fields. In spite of its success, it still suffers from the noise sensitivity problem originating from the assumption that each training point has equal importance or weight in the training process. To relax this problem, the SVM was extended to the fuzzy SVM (FSVM) by applying a fuzzy membership to each training point such that different training points can make different contributions to the learning of the decision surface. Although well-determined fuzzy memberships can improve classification performance, there are no general guidelines for their construction. In this paper, inspired by the centered kernel alignment (CKA), which measures the degree of similarity between two kernels (or kernel matrices), we propose a new fuzzy membership function calculation method in which a heuristic function derived from the CKA is used to calculate the dependence between a data point and its associated label. Although the CKA induced FSVM is similar to the kernel target alignment (KTA) induced FSVM, there is actually a critical difference. Without that centering, the definition of alignment does not correlate well with the performance of learning machines. Extensive experiments are performed on real-world data sets from the UCI benchmark repository and the application domain of computational biology which validate the superiority of the proposed FSVM model in terms of several classification performance measures.

Self-Adjusting Fuzzy Support Vector Machine Based on Analysis of Potential Support Vector Sample Point

Fuzzy support vector machine (FSVM) is a part of machine learning with its good classification effect. So far, there are two most commonly used FSVM models: FSVM on account of class core and fuzzy support vector machine on account of hyperplane that is over class core. Each has its own problems: FSVM on account of class core are dependent on the geometric shape of sample sets. Although FSVM on account of hyperplane that is over class core can solve the above problems to some extent. However, this algorithm has low generalization ability and high time complexity. Therefore, Inspired by these two common models, the paper proposes an improved membership function method. By analyzing and calculating the potential support vector sample points, adjustment factor is obtained, which drives the class core to adjust along the direction away from the outliers. In this way, membership of noise and outliers are reduced and the membership of support vector will also increase to some extent. In this paper, a new experimental comparison method is used, which can make the comparison of classification effect more obvious and convincing. The experimental part compares the proposed FSVM model with the above two FSVM models. It shows that the proposed algorithm improves the stability and classification accuracy to some extent.

Identification of suitable membership and kernel function for FCM based FSVM classifier model

Support vector machine (SVM) a new machine learning algorithm emerging as an effective classification technique than neural networks and other statistical classifiers. Its performance is however, observed to be affected by presence of outliers or noise in the dataset. So, to enhance the generalization capability of SVM, researchers focussed on developing fuzzy support vector machines (FSVM), which works on the concept of fuzzy membership to data points for learning the best decision hyperplane to classify data points. In such case, the choice of membership for data points is critical for good behaviour of the model. So, this paper focuses on finding right membership using optimal fuzzy index parameter (m) and demonstrating its significance in membership and classifier performance. This research work focussed on finding membership values using FCM and final membership from derived low value and high value membership function and incorporating them in FSVM model, to find the best membership value from analysis of five datasets used in the study. For linear classification of data, as kernel functions can show its influence, the study also performed analysis on selection of appropriate kernel function that can best fit the FCM based FSVM classifier model and prove enhancement of generalization performance with our research findings. High and low membership functions are compared through computational analyses and the membership that results in better generalization capability in the model is proven in the study.

Influence of fuzzy index parameter on FSVM classifier performance

Support vector machine (SVM), a machine learning algorithm used extensively for pattern analysis and recognition, is found sensitive to outliers and noise. Fuzzy support vector machine (FSVM) has been used in many applications as a most prominent technique by researchers to overcome the sensitivity issue faced by SVM, and for its good generalisation performance. In this research, a method to justify the performance of FSVM classifier by showing the influence of fuzzy index m on membership function of the model has been proposed. In the first phase of the study, an algorithm to find the optimal fuzzy index using fuzzy C-means (FCM) and to avoid testing all fuzzy index values on the FSVM model has been proposed. The operational complexity of the model can be reduced by this process. In the second phase of this study a FSVM algorithm to incorporate new membership function is proposed. The model is tested on six different datasets and kernel functions and the kernel with the highest classification accuracy is identified as an efficient kernel for the applied dataset. The experimental results on the chosen fuzzy index have proven that the proposed alternate methodology enhances classifier accuracy compared to other research findings. Hence, the model could be applied to diverse fields of FSVM applications.

An intuitionistic fuzzy set based S $$^3$$ 3 VM model for binary classification with mislabeled information

Traditionally, robust and fuzzy support vector machine models are used to handle the binary classification problem with noise and outliers. These models in general suffer from the negative effects of having mislabeled training points and disregard position information. In this paper, we propose a novel method to better address these issues. First, we adopt the intuitionistic fuzzy set approach to detect suspectable mislabeled training points. Then we omit their labels but use their full position information to build a semi-supervised support vector machine ( $$\mathrm {S^3VM}$$ ) model. After that, we reformulate the corresponding model into a non-convex problem and design a branch-and-bound algorithm to solve it. A new lower bound estimator is used to improve the accuracy and efficiency for binary classification. Numerical tests are conducted to compare the performances of the proposed method with other benchmark support vector machine models. The results strongly support the superior performance of the proposed method.