Fuzzy Support Vector Machine Research Articles

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.

Automatic liver tumor detection and classification using the hyper tangent fuzzy C-Means and improved fuzzy SVM

Automatic liver tumor detection and classification using the hyper tangent fuzzy C-Means and improved fuzzy SVM

Diagnosis of Alzheimer’s disease via Intuitionistic fuzzy least squares twin SVM

Neurodegenerative disorders like Alzheimer’s disease (AD) are irreversible and show atrophies in the area of the cerebral cortex of brain. AD leads to loss of memory and other cognitive impairments. The AD subjects are evaluated based on magnetic resonance imaging scans. The data may have the problem of class imbalance, noise and outliers which is a great challenge for classification. Support vector machines and twin support vector machine-based classifiers may not effectively deal with these problems as both these models assume that all the samples are equally important for the separating hyperplane. To overcome these issues, we propose intuitionistic fuzzy least square twin support vector machine for class imbalance problems (IFLSTSVM) and class specific-IFLSTSVM (CS-IFLSTSVM). To minimize the effects of class imbalance, the samples are appropriately weighted to minimize their effect on the optimal hyperplane. Moreover, we use intuitionistic fuzzy scores to overcome the issues of noise and outliers. Intuitionistic fuzzy score values generate appropriate weights by considering both the distance of the samples from the class centroid as well as the heterogeneity of the samples. The proposed models IFLSTSVM and CS-IFLSTSVM are efficient as they need to solve a system of linear equations. In Alzheimer’s disease diagnosis, the proposed IFLSTSVM and CS-IFLSTSVM models showed better performance in MCI_vs_AD and CN_vs_MCI cases, respectively. Moreover, the proposed models showed better performance in the diagnosis of breast cancer classification. The statistical analysis carried out over KEEL and UCI data leads to the superiority of the proposed models. The source code of the proposed model is available at https://github.com/mtanveer1/Diagnosis-of-Alzheimer-s-disease-via-Intuitionistic-fuzzy-least-squares-twin-SVM.

Improved Cyber Attack Detection Using MLB-FQS: A Novel Modified Lagrange Butterfly-based Fuzzy Quasi-Linear SVM Algorithm

With the rapid enhancement of communication and computer networks, internet-based technologies provide numerous convenient and accessible services to the public all over the world. Meanwhile, the types and number of cyber attacks have been increasing day by day and create a serious threat to secure most confidential data. Hence, securing data is considered a crucial task and nowadays intrusion detection system is one of the most effective and promising techniques to provide secure data by detecting malicious data. However, it becomes extremely hard in preventing the network system from attack due to imbalanced data that results in low detection and poor accuracy rates. Therefore, a novel intrusion detection mechanism is proposed to accurately detect cyber intrusion with high detection accuracy rate. The Intrusion detection mechanism comprises three significant phases namely the data pre-processing phase, the feature selection phase as well as attack detection phase. Various pre-processing techniques namely data normalization, data conversion as well as one-hot encoding process are utilized to provide an appropriate format that is suitable for analysis. In the feature selection phase, a Modified Lagrange Butterfly (MLB) optimization is employed to select optimal features for effective detection. Finally, a Fuzzy Quasi-Linear Support Vector Machine (FQL-SVM) classifier is employed to determine the network as two different types of classes namely the abnormal or malicious classes. After examining on malware dataset, the experimental analysis demonstrates a high classification accuracy rate of 98.41% which is better than other comparative techniques.

Opt-RNN-DBFSVM: Optimal recurrent neural network density based fuzzy support vector machine

Two major problems are encountered when using fuzzy SVM: (a) the number of local minima increases exponentially with the number of samples and (b) the quantity of required computer storage, required for a regular quadratic programming solver, increases by an exponential magnitude as the problem size expands. The Kernel-Adatron family of algorithms gaining attention lately which has allowed to handle very large classification and regression problems. However, these methods treat different types of samples (Noise, border, and core) with the same manner, which causes searches in unpromising areas and increases the number of iterations. In this work, we introduce a hybrid method to overcome these shortcoming, namely Optimal Recurrent Neural Network Density Based fuzzy Support Vector Machine (Opt-RNN-DBFSVM). This method consists of four steps: (a) characterization of different samples, (b) elimination of samples with a low probability of being a support vector, (c) construction of an appropriate recurrent neural network based on an original energy function, and (d) solution of the system of differential equations, managing the dynamics of the RNN, using the Euler–Cauchy method involving an optimal time step. Thanks to its recurrent architecture, the RNN remembers the regions explored during the search process. We demonstrated that RNN-FSVM converges to feasible support vectors and Opt-RNN-DBFSVM has a very low time complexity compared to RNN-FSVM with constant time step, and KAs-FSVM. Several experiments were performed on academic data sets. We used several classification performance measures to compare Opt-RNN-DBFSVM to different classification methods and the results obtained show the good performance of the proposed method.

A classification method of fuzzy semi-supervised support vector machines for the problems of imbalance

Positive instances are often significantly less than negative instances in real-world classification problems. However, positive categories are typically more relevant to the primary focus of categorization tasks. Moreover, obtaining labeled data is often expensive, and the majority of real-life data is unlabeled. Therefore, semi-supervised learning has become a popular approach for addressing imbalanced problems. Traditional support vector machines (SVMs) treat all samples equally and are not suitable for semi-supervised learning. To address this issue, a semi-supervised model called the fuzzy semi-supervised SVM (FS3VM) has been proposed. The FS3VM model uses the degree of entropy-based fuzzy membership to ensure the materiality of positive classes by assigning positive instances to relatively large degrees of fuzzy membership. After introducing the mainstream FS3VM model, the fundamental theory and methods of the model are discussed and expanded upon, including the FS3VM algorithm, which applies the Sequential Minimal Optimization (SMO) algorithm to the dual problem. The proposed FS3VM model is a smooth and continuous optimization problem, and its dual is a standard quadratic programming. Experimental results demonstrate that the proposed FS3VM model outperforms other compared learning algorithms.

Dense fuzzy support vector machine to binary classification for imbalanced data

Majority classes are easily to be found in imbalance datasets, instead, minority classes are hard to be paid attention to due to the number of is rare. However, most existing classifiers are better at exploring majority classes, resulting in that classification results are unfair. To address this issue of binary classification for imbalance data, this paper proposes a novel fuzzy support vector machine. The thought is that we trained two support vector machines to learn the majority class and the minority class, respectively. Then, the proposed fuzzy is used to estimate the assistance provided by instance points for the training of the support vector machines. Finally, it can be judged for unknown instance points through evaluating that they provided the assistance to the training of the support vector machines. Results on the ten UCI datasets show that the class accuracy of the proposed method is 0.747 when the imbalanced ratio between the classes reaches 87.8. Compare with the competitors, the proposed method wins over them in classification performance. We find that aiming at the classification of imbalanced data, the complexity of data distribution has negative effects on classification results, while fuzzy can resist these negative effects. Moreover, fuzzy can assist those classifiers to gain superior classification boundaries.

Hybrid fuzzy support vector machine approach for Coconut tree classification using image measurement

Classification of trees based on morphological parameters is important for identification, taxonomy, ecological research, conservation etc. The manual measurement of these parameters is tedious, laborious, and inconsistent. Even though there are many tree classification methods based on the morphological parameters available in the literature, there is hardly any research in classification of Coconut trees based on morphological parameters. This research proposes a novel hybrid fuzzy support vector machine approach for classification of Coconut trees based on three critical morphological parameters such as height, inclination, and orientation. A fuzzy logic-based parameter measurement from image is used to estimate critical morphological parameters the from the images. An original dataset of 17000 images of Indian west coast tall Coconut trees is created to train and test the proposed method. The classification for each of the three parameters is carried out separately using fuzzy logic and support vector machine and the best classification is chosen using confidence voting method. The final classification of the Coconut trees based on all the three parameters is obtained using weight-based indexing by combining the individual outcome of confidence voting method. The results showed that the proposed architecture gives better performance than state-of-the-art classifiers, with an accuracy of 88.86%. The statistical analysis showed that the proposed architecture is stable and robust than other methods, gives least confidence interval value of ±2.5, ±3.2, ±1.9 for three different datasets other than the original dataset.

Intuitionistic Fuzzy Weighted Least Squares Twin SVMs.

Fuzzy membership is an effective approach used in twin support vector machines (SVMs) to reduce the effect of noise and outliers in classification problems. Fuzzy twin SVMs (TWSVMs) assign membership weights to reduce the effect of outliers, however, it ignores the positioning of the input data samples and hence fails to distinguish between support vectors and noise. To overcome this issue, intuitionistic fuzzy TWSVM combined the concept of intuitionistic fuzzy number with TWSVMs to reduce the effect of outliers and distinguish support vectors from noise. Despite these benefits, TWSVMs and intuitionistic fuzzy TWSVMs still suffer from some drawbacks as: 1) the local neighborhood information is ignored among the data points and 2) they solve quadratic programming problems (QPPs), which is computationally inefficient. To overcome these issues, we propose a novel intuitionistic fuzzy weighted least squares TWSVMs for classification problems. The proposed approach uses local neighborhood information among the data points and also uses both membership and nonmembership weights to reduce the effect of noise and outliers. The proposed approach solves a system of linear equations instead of solving the QPPs which makes the model more efficient. We evaluated the proposed intuitionistic fuzzy weighted least squares TWSVMs on several benchmark datasets to show the efficiency of the proposed model. Statistical analysis is done to quantify the results statistically. As an application, we used the proposed model for the diagnosis of Schizophrenia disease.

Phase diagram combined with improved fuzzy support vector machine for rapid and nondestructive detection of Diarrhetic shellfish poisoning [pdf

Phase diagram combined with improved fuzzy support vector machine for rapid and nondestructive detection of Diarrhetic shellfish poisoning [pdf

Phase diagram combined with improved fuzzy support vector machine for rapid and nondestructive detection of Diarrhetic shellfish poisoning

Phase diagram combined with improved fuzzy support vector machine for rapid and nondestructive detection of Diarrhetic shellfish poisoning

Comprehensive Evaluation of Marine Ship Fires Risk Based on Fuzzy Broad Learning System

Ship fires exhibit the main characteristics of a high possibility of occurrence, large load, fast spreading, high difficulty in extinguishing, and serious losses. Therefore, once a fire occurs, it will cause huge loss in terms of economic and personnel safety. Firstly, a ship fire risk evaluation indicator system was constructed based on the causes and severity of the fires. Secondly, a comprehensive evaluation method for the fuzzy broad learning system (FBLS) was proposed. The fuzzy system was used to implement feature mapping on the input data, and the extracted fuzzy features were further input into the BLS enhancement layer. A fuzzy broad learning neural network structure was constructed by combining fuzzy features, feature nodes, and enhancement nodes. The method was applied to the field of risk assessment for the first time, and is a reference for subsequent studies. Finally, the risk levels of ship fires were classified and compared with evaluation methods such as fuzzy support vector machine (FSVM) and Fuzzy BP neural network (FBPNN) to demonstrate effectiveness and accuracy. The proposed FBLS method was used to predict actual cases, and the results showed consistency with the level determined by the accident investigation report published by the Maritime Bureau Administration.

Research on performance evaluation and optimization of college budget management under the background of big data

Abstract At present, due to the limitations of technology and resources, the comprehensive budget management generally has problems such as imperfect foundation of establishment, weak binding force of execution, and lack of performance appraisal system. These problems become more prominent in the context of the era of big data. The use of big data technology, such as large capacity, real-time control, intelligent Settings and other characteristics, will be able to improve the current many problems. Therefore, this paper focuses on how to combine big data with comprehensive budget management. This paper holds that it is not enough for comprehensive budget management to stay in the current stage, but also need to use big data technology, which is a higher level of information stage. Therefore, this paper adopts the method of double subordination fuzzy support vector machine to study the effect of budget management of scientific research projects in Chinese universities. Based on the construction of comprehensive budget management performance evaluation index system throughout the whole process of college budget cycle, taking S university as an example, this paper analyzes the performance of comprehensive budget management in S university, and points out that big data can optimize and innovate the budget management mode, which has great significance for college budget management.

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.

Research on the Evaluation System of Urban Rail Transit Operation Safety in the Context of Intelligent Transportation

<div>With the rapid development of the Internet and intelligent control technology, intelligent transportation has become a research hotspot in building a smart city. Under the background of intelligent transportation, it is particularly important to effectively evaluate the rail transit as the framework of urban public transport in this study, and fuzzy mechanism is introduced to optimize the support vector machine (SVM), and on this basis, analytic hierarchy process (AHP) and SVM are combined to improve the classification accuracy and improve the rail transit operation safety evaluation index system. The experimental results show that the classification accuracy of the fuzzy SVM combined with AHP is above 85% on all the datasets, and it can effectively eliminate the less-relevant indicators. In the actual evaluation of Shanghai Rail Transit safety, the prediction accuracy exceeded 80% and the highest reached 94.51%. Among them, the accuracy of management level and infrastructure were increased by 24.1% and 18.34%, respectively, indicating that this method can effectively screen the evaluation indicators. In the evaluation of Beijing Rail Transit, the accuracy rate of the combined algorithm reaches 95.67%, with high classification accuracy, which provides a reference direction for the establishment of the rail transit operation safety evaluation system.</div>

A new fuzzy support vector machine with pinball loss

The fuzzy support vector machine (FSVM) assigns each sample a fuzzy membership value based on its relevance, making it less sensitive to noise or outliers in the data. Although FSVM has had some success in avoiding the negative effects of noise, it uses hinge loss, which maximizes the shortest distance between two classes and is ineffective in dealing with feature noise near the decision boundary. Furthermore, whereas FSVM concentrates on misclassification errors, it neglects to consider the critical within-class scatter minimization. We present a Fuzzy support vector machine with pinball loss (FPin-SVM), which is a fuzzy extension of a reformulation of a recently proposed support vector machine with pinball loss (Pin-SVM) with several significant improvements, to improve the performance of FSVM. First, because we used the squared L2- norm of errors variables instead of the L1 norm, our FPin-SVM is a strongly convex minimization problem; second, to speed up the training procedure, solutions of the proposed FPin-SVM, as an unconstrained minimization problem, are obtained using the functional iterative and Newton methods. Third, it is proposed to solve the minimization problem directly in primal. Unlike FSVM and Pin-SVM, our FPin-SVM does not require a toolbox for optimization. We dig deeper into the features of FPin-SVM, such as noise insensitivity and within-class scatter minimization. We conducted experiments on synthetic and real-world datasets with various sounds to validate the usefulness of the suggested approach. Compared to the SVM, FSVM, and Pin-SVM, the presented approaches demonstrate equivalent or superior generalization performance in less training time.

Inculcating Context for Emoji Powered Bengali Hate Speech Detection using Extended Fuzzy SVM and Text Embedding Models

The massive growth of social webs offer opportunities to communicate with diverse languages, unstructured text, informal posts, misspelled contents and emojis. Social media users feel comfortable to express their emotions specially emotions with high intensity (hate speech) in their mother tongue. Hate speech in any form targets groups and individuals that may trigger antisocial activities, hate crimes, and terrorist acts. Bengali social media users use Bengali for posting implicit or indirect hate text. Existing Bengali hate speech detection research considers explicit hate speech detection but in actual hate is expressed more in implicit way. In order to detect both implicit and explicit hate speech from low resource content, social webs need highly efficient automated tools. Researchers applied discriminative learning approaches (i.e. SVM, MLP, CNN) to distinguish hate text with only clear-cut outcomes in detecting direct hate speech. The proposed novel Bengali hate speech detection model considers two parallel approaches: (i) It applies extended fuzzy SVM classifier for class imbalanced dataset (FSVMCIL) and multilingual BERT (mBERT) text embedding model to detect first hate label; (ii) Morphological analysis method to detect implicit and explicit hate content with the hate similarity (HS) scheme for second hate label. Linking both labeling methods, this research extracts contextual Bengali hate speech from informal text. This novel HS method considers Word2Vec word embedding model and Bengali hate lexicon. It also considers emoji to text conversion for efficient contextual analysis. This study also conducts extensive experiments for various categories with the Bengali hate speech dataset. It also evaluates the proposed model performance considering weighted F1 score, precision, recall and accuracy parameters. Results reveal significant improvement in Bengali hate speech detection with 2.35% increase in F1- score and 9.11 % increase in accuracy.

Analysis of the Improvement of Engineering Mechanics Experimental Methods Based on IoT and Machine Learning

With the rapid development of sensor technology, machine learning, and the Internet of Things, wireless sensor networks have gradually become a research hotspot. In order to improve the data fusion performance of wireless sensor networks and ensure network security in the event of external attacks, this paper proposes a wireless sensor optimization algorithm model, involving wireless sensor networks, the Internet of Things, and other related fields. This paper first analyzes the role of the Internet of Things in wireless sensor networks, studies the localization mechanism and hierarchy of the Internet of Things based on wireless sensor networks, and improves the LE-RLPCCA (Position Estimation Robust Local Retention Criteria Correlation Analysis) localization algorithm model based on sensor grids. This paper discusses the problems of machine learning in wireless sensor networks, constructs a sensor-based machine learning model, and designs a data fusion algorithm for a wireless sensor networks' machine learning model. The application of wireless sensors in engineering mechanics experiments is summarized, and the optimization algorithm model of the wireless sensor in engineering mechanics experiments is proposed. The analysis results show that the average accuracy of the DKFCM-FSVM (Density aware Kernel-based Fuzzy C-means Clustering algorithm Fuzzy Support Vector Machine) algorithm in detecting five behaviors is 0.997, 0.992, 0.904, 0.996, and 0.946, respectively, and the accuracy in detecting different behaviors is the best, 0.005, 0.01, 0.003, and 0.006 respectively. It achieves the lowest false positive rate in the detection of different behaviors, and the average false positive rate is 0.004, 0.003, 0.003, 0.008, and 0.005, respectively, which shows that the DKFCM-FSVM algorithm model of wireless sensor networks in engineering mechanics experiments is the optimal solution. The work of this paper has good reference value for the application of wireless sensor networks and the optimization of engineering mechanics experimental methods and is helpful for further research of sensor technology.

Implementation of art therapy assisted by the internet of medical things based on blockchain and fuzzy set theory

This work aims to improve the stability and efficiency of the Internet of Medical Things (IoMT), strengthen the security and privacy of personal information, and create a healthy and safe network environment. First, art therapy in the current medical environment is discussed, and the security defenses of the IoMT supporting art therapy are studied. Then, a security defense method for IoMT is proposed by combining basic Blockchain knowledge with the Fuzzy Sets Theory. Specifically, a Density-awarded Fuzzy C-means (DFCM) algorithm is developed to solve the clustering issue of unbalanced data sets. Besides, the Fuzzy Support Vector Machine (FSVM) model is used to classify data points far from the sample center to increase classification accuracy. In the simulation experiment, the DFCM algorithm achieves a pleasing clustering effect on the four data sets, involving the data sets with density changes and imbalanced sample points within the cluster. Moreover, the combination of DFCM and FSVM (DFCM-FSVM) for attack detection in the Internet of Things (IoT) realizes the optimal accuracy in the detection of different behaviors compared with similar algorithms; the detection accuracy of normal behavior, scheduled attack, gray hole attack, and black hole attack is 99.8%, 89.8%, 99.4%, and 91.5%, respectively. This work creatively proposes a hybrid intrusion detection method based on fuzzy theory. As the second line of defense in security protection, it plays a critical role and is suitable for the network environment of the perception layer. In conclusion, the attack detection method based on Blockchain combined with the Fuzzy Sets Theory can precisely identify potential attacks in abnormal data, which has important practical value for maintaining data security and the regular operation of the IoMT.