Fuzzy Classification Research Articles

A Study on the Linguistic Explanation of Online Classification Mechanism Using Confidence-Weighted Learning Fuzzy Classifiers

Fuzzy classifiers can linguistically explain its mechanism while achieving high classification accuracy. In this paper, we aim to explain the classification mechanism in dynamic environments where the classification boundary changes over time. For this purpose, we propose an online updating of fuzzy classifiers by means of Confidence-Weighted Learning. We have confirmed that the model can linguistically explain the classification mechanism in dynamic environments by examining how the weights of the fuzzy If-Then rules change.

Healthcare Monitoring using Machine Learning Based Data Analytics

In this paper, we develop a machine learning based healthcare monitoring and analytics from various Internet of Medical Things (IoMT) devices for possible prediction of cardiovascular risk in patients. The study uses random forest for feature selection and then the fuzzy logic classifier is used for prediction of Cardio Vascular Disease (CVD). The simulation is conducted to test the efficacy of the proposed machine learning based data analytics model over various other methods. The results show than the proposed method has higher rate of classification accuracy in classifying the CVD with higher recall and F1-score than other methods.

Автоматизация процесса контроля ознакомления с документами в организациях с иерархической структурой

The article highlights the process of modeling an organization with a hierarchical structure of management. The process of employees’ manual inspecting the local regulatory documents is considered. There are defined the basic steps to be performed by the participants in the process of familiarization with the documents. According to the selected actions, there has been proposed a method of familiarizing the employees using the two types of tasks: inspecting and forwarding. There is described the process of manual inspecting the documents by the employees using a task-based method. There has been built a diagram for manual selecting the group and subgroups of employees, whose activities are mentioned in the document, by their head. The stages of the manual process of determining the recipients of the document, which can be automated by using different classification methods, are emphasized. The main steps necessary to determine the directions of the document are singled out: defining the keywords and their comparing with actual directions. Modeling the stage of determining the directions of the document was carried out by using classification methods with keywords. The main methods that can be used to determine the directions and keywords of the document are described. Modeling the stage of identifying groups and employees whose activities are appointed by the document was carried out using fuzzy classification methods. The main methods necessary for determining the recipients are highlighted, and the possibilities for extending the model are described. A diagram of automated determining the recommended subgroups and employees wanting to send the documents based on the proposed software models has been built. The prospects of further developing of the proposed scheme of automated inspecting the documents by the employees have been determined.

Stochastic Configuration Based Fuzzy Inference System with Interpretable Fuzzy Rules and Intelligence Search Process

In this paper, a stochastic configuration based fuzzy inference system with interpretable fuzzy rules (SCFS-IFRs) is proposed to improve the interpretability and performance of the fuzzy inference system and determine autonomously an appropriate model structure. The proposed SCFS-IFR first accomplishes a fuzzy system through interpretable linguistic fuzzy rules (ILFRs), which endows the system with clear semantic interpretability. Meanwhile, using an incremental learning method based on stochastic configuration, the appropriate architecture of the system is determined by incremental generation of ILFRs under a supervision mechanism. In addition, the particle swarm optimization (PSO) algorithm, an intelligence search technique, is used in the incremental learning process of ILFRs to obtain better random parameters and improve approximation accuracy. The performance of SCFS-IFRs is verified by regression and classification benchmark datasets. Regression experiments show that the proposed SCFS-IFRs perform best on 10 of the 20 data sets, statistically significantly outperforming the other eight state-of-the-art algorithms. Classification experiments show that, compared with the other six fuzzy classifiers, SCFS-IFRs achieve higher classification accuracy and better interpretation with fewer rules.

Automated Estimation of Crop Yield Using Artificial Intelligence and Remote Sensing Technologies.

Agriculture is the backbone of any country, and plays a viable role in the total gross domestic product (GDP). Healthy and fruitful crops are of immense importance for a government to fulfill the food requirements of its inhabitants. Because of land diversities, weather conditions, geographical locations, defensive measures against diseases, and natural disasters, monitoring crops with human intervention becomes quite challenging. Conventional crop classification and yield estimation methods are ineffective under unfavorable circumstances. This research exploits modern precision agriculture tools for enhanced remote crop yield estimation, and types classification by proposing a fuzzy hybrid ensembled classification and estimation method using remote sensory data. The architecture enhances the pooled images with fuzzy neighborhood spatial filtering, scaling, flipping, shearing, and zooming. The study identifies the optimal weights of the strongest candidate classifiers for the ensembled classification method adopting the bagging strategy. We augmented the imagery datasets to achieve an unbiased classification between different crop types, including jute, maize, rice, sugarcane, and wheat. Further, we considered flaxseed, lentils, rice, sugarcane, and wheat for yield estimation on publicly available datasets provided by the Food and Agriculture Organization (FAO) of the United Nations and the Word Bank DataBank. The ensemble method outperformed the individual classification methods for crop type classification on an average of 13% and 24% compared to the highest gradient boosting and lowest decision tree methods, respectively. Similarly, we observed that the gradient boosting predictor outperformed the multivariate regressor, random forest, and decision tree regressor, with a comparatively lower mean square error value on yield years 2017 to 2021. Further, the proposed architecture supports embedded devices, where remote devices can adopt a lightweight classification algorithm, such as MobilenetV2. This can significantly reduce the processing time and overhead of a large set of pooled images.

Grey fuzzy prediction model of soil organic matter content using hyper-spectral data

PurposeIn order to overcome the uncertainty and improve the accuracy of spectral estimation, this paper aims to establish a grey fuzzy prediction model of soil organic matter content by using grey theory and fuzzy theory.Design/methodology/approachBased on the data of 121 soil samples from Zhangqiu district and Jiyang district of Jinan City, Shandong Province, firstly, the soil spectral data are transformed by spectral transformation methods, and the spectral estimation factors are selected according to the principle of maximum correlation. Then, the generalized greyness of interval grey number is used to modify the estimation factors of modeling samples and test samples to improve the correlation. Finally, the hyper-spectral prediction model of soil organic matter is established by using the fuzzy recognition theory, and the model is optimized by adjusting the fuzzy classification number, and the estimation accuracy of the model is evaluated using the mean relative error and the determination coefficient.FindingsThe results show that the generalized greyness of interval grey number can effectively improve the correlation between soil organic matter content and estimation factors, and the accuracy of the proposed model and test samples are significantly improved, where the determination coefficient R2 = 0.9213 and the mean relative error (MRE) = 6.3630% of 20 test samples. The research shows that the grey fuzzy prediction model proposed in this paper is feasible and effective, and provides a new way for hyper-spectral estimation of soil organic matter content.Practical implicationsThe research shows that the grey fuzzy prediction model proposed in this paper can not only effectively deal with the three types of uncertainties in spectral estimation, but also realize the correction of estimation factors, which is helpful to improve the accuracy of modeling estimation. The research result enriches the theory and method of soil spectral estimation, and it also provides a new idea to deal with the three kinds of uncertainty in the prediction problem by using the three kinds of uncertainty theory.Originality/valueThe paper succeeds in realizing both the grey fuzzy prediction model for hyper-spectral estimating soil organic matter content and effectively dealing with the randomness, fuzziness and grey uncertainty in spectral estimation.

BSRWPSO-FKNN: A boosted PSO with fuzzy K-nearest neighbor classifier for predicting atopic dermatitis disease.

Atopic dermatitis (AD) is an allergic disease with extreme itching that bothers patients. However, diagnosing AD depends on clinicians' subjective judgment, which may be missed or misdiagnosed sometimes. This paper establishes a medical prediction model for the first time on the basis of the enhanced particle swarm optimization (SRWPSO) algorithm and the fuzzy K-nearest neighbor (FKNN), called bSRWPSO-FKNN, which is practiced on a dataset related to patients with AD. In SRWPSO, the Sobol sequence is introduced into particle swarm optimization (PSO) to make the particle distribution of the initial population more uniform, thus improving the population's diversity and traversal. At the same time, this study also adds a random replacement strategy and adaptive weight strategy to the population updating process of PSO to overcome the shortcomings of poor convergence accuracy and easily fall into the local optimum of PSO. In bSRWPSO-FKNN, the core of which is to optimize the classification performance of FKNN through binary SRWPSO. To prove that the study has scientific significance, this paper first successfully demonstrates the core advantages of SRWPSO in well-known algorithms through benchmark function validation experiments. Secondly, this article demonstrates that the bSRWPSO-FKNN has practical medical significance and effectiveness through nine public and medical datasets. The 10 times 10-fold cross-validation experiments demonstrate that bSRWPSO-FKNN can pick up the key features of AD, including the content of lymphocytes (LY), Cat dander, Milk, Dermatophagoides Pteronyssinus/Farinae, Ragweed, Cod, and Total IgE. Therefore, the established bSRWPSO-FKNN method practically aids in the diagnosis of AD.

Comparative Study of Fuzzy Rule-Based Classifiers for Medical Applications.

The use of machine learning in medical decision support systems can improve diagnostic accuracy and objectivity for clinical experts. In this study, we conducted a comparison of 16 different fuzzy rule-based algorithms applied to 12 medical datasets and real-world data. The results of this comparison showed that the best performing algorithms in terms of average results of Matthews correlation coefficient (MCC), area under the curve (AUC), and accuracy (ACC) was a classifier based on fuzzy logic and gene expression programming (GPR), repeated incremental pruning to produce error reduction (Ripper), and ordered incremental genetic algorithm (OIGA), respectively. We also analyzed the number and size of the rules generated by each algorithm and provided examples to objectively evaluate the utility of each algorithm in clinical decision support. The shortest and most interpretable rules were generated by 1R, GPR, and C45Rules-C. Our research suggests that GPR is capable of generating concise and interpretable rules while maintaining good classification performance, and it may be a valuable algorithm for generating rules from medical data.

A fuzzy classifier for evaluation of research topics by using keyword co-occurrence network and sponsors information

A fuzzy classifier for evaluation of research topics by using keyword co-occurrence network and sponsors information

Importance analysis of decision making factors based on fuzzy decision trees

Classification is one important and commonly used Machine Learning technique. The classification effectiveness can be influenced by different factors and initial data used for the classifier induction and in the process of classification, including. The quality of measurements of the input attributes of new samples for the classification can affect the accuracy and reliability of the classification result. At the same time, the degree of influence of different attributes is not the same. There are some attributes that are most important for the classification because have a greater influence on the classification result than others. A new method for the determination of the most important attributes is proposed. This method is developed based on the approach of Importance Analysis, which is widely used in reliability engineering. The proposed method investigates the sensitivity of the classification from the input attributes and indicates the attributes for which changes have the most impact on the classification result. The attribute’s importance is evaluated by the special index which is known as structural importance in reliability engineering. This method application is illustrated in the paper by Fuzzy Decision Tree, but it can be used for any other classifiers induction. The use of a fuzzy classifier allows for taking into consideration of possible uncertainty of initial data.

AERF: Adaptive ensemble random fuzzy algorithm for anomaly detection in cloud computing

The abnormality of system behavior is inevitable in cloud computing because of its complexity and scale. How to perform anomaly detection on the system’s operating data to discover abnormal behavior has become a popular research field. However, anomaly detection is a challenging research problem because data in cloud computing scenarios is continuous, imbalanced, and cannot be acquired at once. In this work, an adaptive ensemble random fuzzy (AERF) algorithm is proposed for anomaly detection in cloud computing systems. The random fuzzy rule-based method in the AERF selects samples randomly to enhance the diversity of base classifiers for dealing with disturbances caused by abnormal sample distribution effectively. A dynamic weighting strategy is used for fuzzy classifier ensembles to improve processing efficiency and anomaly detection accuracy. Experimental results show that the AERF yields 10% higher AUC and G-mean than existing methods on SMD and EMOSCloud datasets and 20% higher AUC and G-mean on five benchmarking datasets. The AERF yields 20% higher F1 than existing methods on EMOSCloud dataset. For all datasets, the AERF yields 50% faster training time than other methods.

Weak-Strong Self-Adapting Fuzzy Neural Classifier for Dynamic Object Detection in RGBD Videos

This paper presents a fuzzy neural method to model background from videos in order to detect dynamic objects. The method includes a weak fuzzy classifier that performs an initial foreground and background separation based on color and depth differences between the actual frame and background models. The outputs of this fuzzy system are weighted according to the result of the color and depth noise modeling. A degree of uncertainty and the strength of decisions, in combination with the weighting results, are used by the method to define more accurately the dynamic objects through a strong fuzzy classifier. The final stage of foreground detection is implemented with a Discrete-Time Cellular Neural Network to improve the foreground definition. Finally, the color and depth background models are updated based on a fuzzy learning rate strategy. The method was evaluated with the new SBM-RGBD database and compared against several state-of-the-art methods showing a similar or better performance considering the quantitative and qualitative evaluations.

Fuzzy Model for the Automatic Recognition of Human Dendritic Cells

Background and objective: Nowadays, foodborne illness is considered one of the most outgrowing diseases in the world, and studies show that its rate increases sharply each year. Foodborne illness is considered a public health problem which is caused by numerous factors, such as food intoxications, allergies, intolerances, etc. Mycotoxin is one of the food contaminants which is caused by various species of molds (or fungi), which, in turn, causes intoxications that can be chronic or acute. Thus, even low concentrations of Mycotoxin have a severely harmful impact on human health. It is, therefore, necessary to develop an assessment tool for evaluating their impact on the immune response. Recently, researchers have approved a new method of investigation using human dendritic cells, yet the analysis of the geometric properties of these cells is still visual. Moreover, this type of analysis is subjective, time-consuming, and difficult to perform manually. In this paper, we address the automation of this evaluation using image-processing techniques. Methods: Automatic classification approaches of microscopic dendritic cell images are developed to provide a fast and objective evaluation. The first proposed classifier is based on support vector machines (SVM) and Fisher's linear discriminant analysis (FLD) method. The FLD-SVM classifier does not provide satisfactory results due to the significant confusion between the inhibited cells on one hand, and the other two cell types (mature and immature) on the other hand. Then, another strategy was suggested to enhance dendritic cell recognition results that are emitted from microscopic images. This strategy is mainly based on fuzzy logic which allows us to consider the uncertainties and inaccuracies of the given data. Results: These proposed methods are tested on a real dataset consisting of 421 images of microscopic dendritic cells, where the fuzzy classification scheme efficiently improved the classification results by successfully classifying 96.77% of the dendritic cells. Conclusions: The fuzzy classification-based tools provide cell maturity and inhibition rates which help biologists evaluate severe health impacts caused by food contaminants.

Fuzzy similarity phrases for interpretable data classification

Data classification is a core component of many intelligent systems, and it has been almost monopolizing the interest of the scientific community for decades. However, developing classifiers capable of explaining how a classification result was derived, in a way that is compatible to the human perception, is still a challenge. Steps towards this direction have been made via interpretable fuzzy classifiers capable of extracting linguistically expressible rules from data. Motivated by the need for interpretable data classification, this study proposes a novel interpretable fuzzy classification framework based on Fuzzy Similarity Phrases (FSPs), aiming to provide an improved tradeoff between interpretability and classification performance. This framework incorporates the following novel components: a) a generic vocabulary-based feature extraction scheme which considers similarity features and fuzzy sets to construct FSPs; b) a rule generation methodology based on FSPs; c) an embedded feature selection methodology targeting to keep both the number of extracted rules and their length low; and an interpretable class prediction mechanism based on the interpretation of the FSPs. The experimental evaluation of the proposed framework on various publicly available datasets, in comparison with well-known and state-of-the-art classifiers, validate that it can provide a better average classification performance with fewer rules.

Fuzzy logic-based vehicle safety estimation using V2V communications and on-board embedded ROS-based architecture for safe traffic management system in hail city

<abstract> <p>Estimating the state of surrounding vehicles is crucial to either prevent or avoid collisions with other road users. However, due to insufficient historical data and the unpredictability of future driving tactics, estimating the safety status is a difficult undertaking. To address this problem, an intelligent and autonomous traffic management system based on V2V technology is proposed. The main contribution of this work is to design a new system that uses a real-time control system and a fuzzy logic algorithm to estimate safety. The robot operating system (ROS) is the foundation of the control architechture, which connects all the various system nodes and generates the decision in the form of a speech and graphical message. The safe path is determined by a safety evaluation system that combines sensor data with a fuzzy classifier. Moreover, the suitable information processed by each vehicle unit is shared in the group to avoid unexpected problems related to speed, sudden braking, unplanned deviation, street holes, road bumps, and any kind of street issues. The connection is provided through a network based on the ZigBee protocol. The results of vehicle tests show that the proposed method provides a more reliable estimate of safety as compared to other methods.</p> </abstract>

Optimized Framework for Signature Recognition Using Genetic Algorithm, Loci Method, and Fuzzy Classifier

Optimized Framework for Signature Recognition Using Genetic Algorithm, Loci Method, and Fuzzy Classifier

Нечеткий классификатор типа Min-Max: обзор

Online adaptation and interpretability have become one of the important requirements for machine learning models. Popular models such as artificial neural networks cannot fully implement them. Fuzzy classifiers of the Min-Max type are interpretable, thanks to the underlying fuzzy logic theory, and adaptable with the advent of new information. This article presents a comprehensive literature review on machine learning models based on fuzzy Min-Max classifiers. The architecture of the classifier and the principle of its operation are presented. A review of the modifications of the classifier is carried out and their effectiveness is evaluated. Applications of the classifier and its modifications in solving real problems are indicated. In conclusion, statements are drawn about the work of the classifier and the problems that have remained unresolved.

Fuzzy Classifications in the Typological Analysis of Workers by Employment Instability

Fuzzy Classifications in the Typological Analysis of Workers by Employment Instability

Logistic Regression Trust–A Trust Model for Internet-of-Things Using Regression Analysis

Internet of Things (IoT) is a popular social network in which devices are virtually connected for communicating and sharing information. This is applied greatly in business enterprises and government sectors for delivering the services to their customers, clients and citizens. But, the interaction is successful only based on the trust that each device has on another. Thus trust is very much essential for a social network. As Internet of Things have access over sensitive information, it urges to many threats that lead data management to risk. This issue is addressed by trust management that help to take decision about trustworthiness of requestor and provider before communication and sharing. Several trust-based systems are existing for different domain using Dynamic weight method, Fuzzy classification, Bayes inference and very few Regression analysis for IoT. The proposed algorithm is based on Logistic Regression, which provide strong statistical background to trust prediction. To make our stand strong on regression support to trust, we have compared the performance with equivalent sound Bayes analysis using Beta distribution. The performance is studied in simulated IoT setup with Quality of Service (QoS) and Social parameters for the nodes. The proposed model performs better in terms of various metrics. An IoT connects heterogeneous devices such as tags and sensor devices for sharing of information and avail different application services. The most salient features of IoT system is to design it with scalability, extendibility, compatibility and resiliency against attack. The existing works finds a way to integrate direct and indirect trust to converge quickly and estimate the bias due to attacks in addition to the above features.

A Comparative Analysis of International and Indonesian Quality Assurance Assessments: Review of Elementary Education Study Program

Quality assurance is an essential issue in higher education, therefore Agency for Quality Assurance through Accreditation of Study Programs (AQAS) and LAMDIK as independent quality assurance institutions in Indonesia are present as widely-known quality assurance institutions in Indonesia with differences between standards or criteria on the instruments of the two institutions. This study is based on a research question about how much similarity the assessment variables of the two quality assurance institutions. Hence, this research aims to comparatively analyze the instrument of the two institutions, to figure out which standards or criteria are the focus of the assessment, as well as to analyze the follow-up for the assessment for further study. The data source used was the assessment criteria of the two institutions therefore the methodology was multi-layered research consisting of narrative content analysis followed by statistical analysis, final expert recommendation review, and expert interviews which produce a comparative description. One of clear findings of this study was that AQAS use top-down model hierarchically, starting from the university, faculty, or school, and then to the study program being assessed. Statistical analysis used was fuzzy classifications such as 0, 0.25, 0.50001, 0.75, and 1 which were interpreted as fully out, more out than in, neither fully in nor fully out, more than out, and fully in. The similarity of standards or assessment criteria between the two institutions was shown by PRI from the results of the analysis using the fs-QCA application which pointed out at 0.66667 or close to 67% exceeding 0.5 which was a significant level of inconsistency. The inconsistency value occurred because there are several differences in AQAS standards and assessment criteria at LAMDIK which are influenced by education policies in Indonesia such as the tri dharma policy of higher education that requires community service.