BGFS: Design and Development of Brain Genetic Fuzzy System for Data Classification

2018 ◽  
Vol 27 (2) ◽  
pp. 231-247 ◽  
Author(s):  
Chandrasekar Ravi ◽  
Neelu Khare
Keyword(s):  
Real Time ◽  
Membership Function ◽  
Optimization Algorithm ◽  
Fuzzy System ◽  
Fuzzy Rule ◽  
Data Classification ◽  
Membership Functions ◽  
Rule Based ◽  
Genetic Fuzzy System ◽  
Brain Storm Optimization

AbstractRecently, classification systems have received significant attention among researchers due to the important characteristics and behaviors of analysis required in real-time databases. Among the various classification-based methods suitable for real-time databases, fuzzy rule-based classification is effectively used by different researchers in various fields. An important issue in the design of fuzzy rule-based classification is the automatic generation of fuzzy if-then rules and the membership functions. The literature presents different techniques for automatic fuzzy design. Among the different techniques available in the literature, choosing the type, the number of membership functions, and defining parameters of membership function are still challenging tasks. In order to handle these challenges in the fuzzy rule-based classification system, this paper proposes a brain genetic fuzzy system (BGFS) for data classification by newly devising the exponential genetic brain storm optimization. Here, membership functions are optimally devised using exponential genetic brain storm optimization algorithm and rules are derived using the exponential brain storm optimization algorithm. The designed membership function and fuzzy rules are then effectively utilized for data classification. The proposed BGFS is analyzed with four datasets, using sensitivity, specificity, and accuracy. The outcome ensures that the proposed BGFS obtained the maximum accuracy of 88.8%, which is high as compared with the existing adaptive genetic fuzzy system.

scholarly journals Brain Storm Optimization Algorithm for Data Classification

2021 ◽  
Author(s):  
Zhifeng Zhang ◽  
Shaolin Zhu ◽  
Tianqi Li ◽  
Baohuan Li
Keyword(s):  
Optimization Algorithm ◽  
Fuzzy System ◽  
Fuzzy Systems ◽  
Inductive Learning ◽  
Fuzzy Rule ◽  
Data Classification ◽  
Search Space ◽  
Exponential Model ◽  
Brain Storm Optimization ◽  
The Brain

Abstract With the increasing of the number of dimensions or variables in the search space, the inductive learning of fuzzy rule classifier will be influenced by the generation and optimization of rules. Thus, the extensibility and accuracy of fuzzy systems will be affected. In this paper, the brain storm optimization algorithm was used. A new fuzzy system was designed by modifying the rules definition process in traditional fuzzy system. In the derivation of rules, the exponential model was introduced to improve the traditional brain storming algorithm. On the basis, this new fuzzy system was used for the research on data classification. The experimental results show that this new fuzzy system can improve the accuracy of data classification.

AUTOMATIC DEVELOPMENT OF FUZZY MEMBERSHIP FUNCTIONS ON HEPATITIS PATIENTS DATA USING PARTICLE SWARM OPTIMIZATION (PSO)

2015 ◽  
Vol 77 (22) ◽  
Author(s):  
Candra Dewi ◽  
Ratna Putri P.S ◽  
Indriati Indriati
Keyword(s):  
Particle Swarm Optimization ◽  
Membership Function ◽  
Fuzzy System ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Membership Functions ◽  
Swarm Optimization ◽  
Global Weight ◽  
The Status ◽  
And Function

Information about the status of disease (prognosis) for patients with hepatitis is important to determine the type of action to stabilize and cure this disease. Among some system, fuzzy system is one of the methods that can be used to obtain this prognosis. In the fuzzification process, the determination of the exact range of membership function will influence the calculation of membership degree and of course will affect the final value of fuzzy system. This range and function can usually be formed using intuition or by using an algorithm. In this paper, Particle Swarm Optimization (PSO) algorithm is implemented to form the triangular membership functions in the case of patients with hepatitis. For testing process, this paper conducts four scenarios to find the best combination of PSO parameter values . Based on the testing it was found that the best parameters to form a membership function range for the hepatitis data is about 0.9, 0.1, 2, 2, 100, 500 for inertia max, inertia min, local ballast constant, global weight constant, the number of particles, and maximum iterations respectively.  

scholarly journals Genetic fuzzy system for prediction of respiratory rate of chicks subject to thermal challenges

2018 ◽  
Vol 22 (6) ◽  
pp. 412-417 ◽  
Author(s):  
Patrícia F. P. Ferraz ◽  
Tadayuki Yanagi Junior ◽  
Yamid F. Hernandez-Julio ◽  
Gabriel A. e S. Ferraz ◽  
Maria A. J. G. Silva ◽  
...  
Keyword(s):  
Respiratory Rate ◽  
Fuzzy Inference System ◽  
Prediction Accuracy ◽  
Fuzzy Inference ◽  
Fuzzy Rule ◽  
System Model ◽  
Inference System ◽  
Rule Based ◽  
Rule Based System ◽  
Genetic Fuzzy System

ABSTRACT The aim of this study was to estimate and compare the respiratory rate (breath min-1) of broiler chicks subjected to different heat intensities and exposure durations for the first week of life using a Fuzzy Inference System and a Genetic Fuzzy Rule Based System. The experiment was conducted in four environmentally controlled wind tunnels and using 210 chicks. The Fuzzy Inference System was structured based on two input variables: duration of thermal exposure (in days) and dry bulb temperature (°C), and the output variable was respiratory rate. The Genetic Fuzzy Rule Based System set the parameters of input and output variables of the Fuzzy Inference System model in order to increase the prediction accuracy of the respiratory rate values. The two systems (Fuzzy Inference System and Genetic Fuzzy Rule Based System) proved to be able to predict the respiratory rate of chicks. The Genetic Fuzzy Rule Based System interacted well with the Fuzzy Inference System model previously developed showing an improvement in the respiratory rate prediction accuracy. The Fuzzy Inference System had mean percentage error of 2.77, and for Fuzzy Inference System and Genetic Fuzzy Rule Based System it was 0.87, thus indicating an improvement in the accuracy of prediction of respiratory rate when using the tool of genetic algorithms.

scholarly journals Fuzzy Rule-Based Classification System for Assessing Coronary Artery Disease

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Reza Ali Mohammadpour ◽  
Seyed Mohammad Abedi ◽  
Somayeh Bagheri ◽  
Ali Ghaemian
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Myocardial Perfusion ◽  
Fuzzy Rule ◽  
Membership Functions ◽  
Classification Result ◽  
Rule Based ◽  
Myocardial Perfusion Scan ◽  
Perfusion Scan ◽  
Artery Disease

The aim of this study was to determine the accuracy of fuzzy rule-based classification that could noninvasively predict CAD based on myocardial perfusion scan test and clinical-epidemiological variables. This was a cross-sectional study in which the characteristics, the results of myocardial perfusion scan (MPS), and coronary artery angiography of 115 patients, 62 (53.9%) males, in Mazandaran Heart Center in the north of Iran have been collected. We used membership functions for medical variables by reviewing the related literature. To improve the classification performance, we used Ishibuchi et al. and Nozaki et al. methods by adjusting the grade of certaintyCFjof each rule. This system includes 144 rules and the antecedent part of all rules has more than one part. The coronary artery disease data used in this paper contained 115 samples. The data was classified into four classes, namely, classes 1 (normal), 2 (stenosis in one single vessel), 3 (stenosis in two vessels), and 4 (stenosis in three vessels) which had 39, 35, 17, and 24 subjects, respectively. The accuracy in the fuzzy classification based on if-then rule was 92.8 percent if classification result was considered based on rule selection by expert, while it was 91.9 when classification result was obtained according to the equation. To increase the classification rate, we deleted the extra rules to reduce the fuzzy rules after introducing the membership functions.

Fuzzy Modeling by Occupancy Degree and Optimal Partition of Projection Using Rough Set Theory

1998 ◽  
Vol 2 (2) ◽  
pp. 54-61
Author(s):  
Youngwan Cho ◽  
◽  
Kichul Lee ◽  
Mignon Park
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Fuzzy System ◽  
Rough Set Theory ◽  
Fuzzy Rule ◽  
Membership Functions ◽  
Linguistic Information ◽  
Input And Output ◽  
Decision Attributes ◽  
Modeling Data

The rough set theory suggested by Pawlak represents the degree of consistency between conditions and decision attributes of data pairs that have no linguistic information. In this paper, by using this representation feature, we define a measure called the occupancy degree that represents the consistency degree of a premise and consequent variables in fuzzy rules describing experimental data pairs. We also propose a method by which we partition the projected data on input space and find an optimal fuzzy rule table and membership functions of input and output variables from data without preliminary linguistic information. We examine the validity of the proposed method by modeling data pairs randomly generated by a fuzzy system.

scholarly journals Optimization Model of Fuzzy Rule Based Expert System Using Max-Min Composition and Schema Mapping Translation

INSIST ◽  
2017 ◽  
Vol 2 (1) ◽  
pp. 30 ◽  
Author(s):  
Hartono Hartono ◽  
Tiarma Simanihuruk
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Expert System ◽  
Membership Function ◽  
Fuzzy Rule ◽  
Fuzzy Decision Making ◽  
Schema Mapping ◽  
Fuzzy Decision ◽  
Rule Based ◽  
Knowledge Based

Abstract— Fuzzy Decision Making involves a process of selecting one or more alternatives or solutions from a finite set of alternatives which suits a set of constraints. In the rule-based expert system, the terms following in the decision making is using knowledge based and the IF Statements of the rule are called the premises, while the THEN part of the rule is called conclusion. Membership function and knowledge based determines the performance of fuzzy rule based expert system. Membership function determines the performance of fuzzy logic as it relates to represent fuzzy set in a computer. Knowledge Based in the other side relates to capturing human cognitive and judgemental processes, such as thinking and reasoning. In this paper, we have proposed a method by using Max-Min Composition combined with Genetic Algorithm for determining membership function of Fuzzy Logic and Schema Mapping Translation for the rules assignment.Keywords— Fuzzy Decision Making, Rule-Based Expert System, Membership Function, Knowledge Based, Max-Min Composition, Schema Mapping Translation

scholarly journals Early Identification of COVID-19 Using Dynamic Fuzzy Rule Based System

2021 ◽  
Vol 8 (5) ◽  
pp. 805-812
Author(s):  
Mohammed Imran Basheer Ahmed ◽  
Atta-ur Rahman ◽  
Mehwash Farooqui ◽  
Fatimah Alamoudi ◽  
Raghad Baageel ◽  
...  
Keyword(s):  
Healthcare Professionals ◽  
Fuzzy Rule ◽  
Fuzzy Membership Function ◽  
Membership Functions ◽  
Dynamic Nature ◽  
Rule Based ◽  
Infected People ◽  
Rule Based System ◽  
Global Pandemic ◽  
The Common

The undergoing research aims to address the problem of COVID-19 which has turned out to be a global pandemic. Despite developing some successful vaccines, the pace has not overcome so far. Several studies have been proposed in the literature in this regard, the present study is unique in terms of its dynamic nature to adapt the rules by reconfigurable fuzzy membership function. Based on patient’s symptoms (fever, dry cough etc.) and history related to travelling, diseases/medications and interactions with confirmed patients, the proposed dynamic fuzzy rule-based system (FRBS) identifies the presence/absence of the disease. This can greatly help the healthcare professionals as well as laymen in terms of disease identification. The main motivation of this paper is to reduce the pressure on the health services due to frequent test assessment requests, in which patients can do the test anytime without the need to make reservations. The main findings are that there is a relationship between the disease and the symptoms in which some symptoms can indicate the probability of the presence of the disease such as high difficulty of breathing, cough, sore throat, and so many more. By knowing the common symptoms, we developed membership functions for these symptoms, and a model generated to distinguish between infected and non-infected people with the help of survey data collected. The model gave an accuracy of 88.78%, precision of 72.22%, sensitivity of 68.42%, specificity of 93.67%, and an f1-score of 69.28%.

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