Pub Date : 2022-01-02DOI: 10.1080/16168658.2021.2019969
V. Jangid, Ganesh Kumar
With the help of various different representations of a hexadecagonal fuzzy number, this paper investigates the uncertainty associated with ambiguity and imprecision in the results of 16-component game scenarios. Numerous membership functions for alpha-cuts are established in terms of symmetrical and asymmetrical situations. We look at the centroid methodology, as well as the mean of the alpha-cut technique, the mean of the bounded area removal method and the bounded area included by the fuzzy number. A new centroid-based technique is used to rank two hexadecagonal fuzzy numbers. Defuzzification approaches have also been used to numerical examples based on fuzzy game theory in order to illustrate the effectiveness of the techniques.
{"title":"Hexadecagonal Fuzzy Numbers: Novel Ranking and Defuzzification Techniques for Fuzzy Matrix Game Problems","authors":"V. Jangid, Ganesh Kumar","doi":"10.1080/16168658.2021.2019969","DOIUrl":"https://doi.org/10.1080/16168658.2021.2019969","url":null,"abstract":"With the help of various different representations of a hexadecagonal fuzzy number, this paper investigates the uncertainty associated with ambiguity and imprecision in the results of 16-component game scenarios. Numerous membership functions for alpha-cuts are established in terms of symmetrical and asymmetrical situations. We look at the centroid methodology, as well as the mean of the alpha-cut technique, the mean of the bounded area removal method and the bounded area included by the fuzzy number. A new centroid-based technique is used to rank two hexadecagonal fuzzy numbers. Defuzzification approaches have also been used to numerical examples based on fuzzy game theory in order to illustrate the effectiveness of the techniques.","PeriodicalId":37623,"journal":{"name":"Fuzzy Information and Engineering","volume":"65 1","pages":"84 - 122"},"PeriodicalIF":1.2,"publicationDate":"2022-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87845223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-02DOI: 10.1080/16168658.2021.1915457
Pallavi Agarwal, Ajay Sharma, Neeraj Kumar
Genetic Algorithm (GA) is an optimized method to find a perfect solution which is based on general genetic process of life cycle. In this article we discussed a crisp and a fuzzy inventory model keeping its demand rate constant for the imprecision and uncertainly deteriorating items with special reference to shortage and partially backlogging systems. The objective of this paper is to minimize the total cost of fuzzy inventory environment for which Graded mean representation, Signed distance and Centroid methods are used to defuzzify the total cost of the systems. Consequently, we are comparing the total average cost, obtained through these methods with the help of numerical example, and sensitively analysis is also given to show the effects of the values on these items. Moreover, Genetic Algorithm (GA) is also applied to the optimistic value of the total cost of the crisp model for the effective and fruitful results.
{"title":"A Soft-Computing Approach to Fuzzy EOQ Model for Deteriorating Items with Partial Backlogging","authors":"Pallavi Agarwal, Ajay Sharma, Neeraj Kumar","doi":"10.1080/16168658.2021.1915457","DOIUrl":"https://doi.org/10.1080/16168658.2021.1915457","url":null,"abstract":"Genetic Algorithm (GA) is an optimized method to find a perfect solution which is based on general genetic process of life cycle. In this article we discussed a crisp and a fuzzy inventory model keeping its demand rate constant for the imprecision and uncertainly deteriorating items with special reference to shortage and partially backlogging systems. The objective of this paper is to minimize the total cost of fuzzy inventory environment for which Graded mean representation, Signed distance and Centroid methods are used to defuzzify the total cost of the systems. Consequently, we are comparing the total average cost, obtained through these methods with the help of numerical example, and sensitively analysis is also given to show the effects of the values on these items. Moreover, Genetic Algorithm (GA) is also applied to the optimistic value of the total cost of the crisp model for the effective and fruitful results.","PeriodicalId":37623,"journal":{"name":"Fuzzy Information and Engineering","volume":"24 3","pages":"1 - 15"},"PeriodicalIF":1.2,"publicationDate":"2022-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72470128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-02DOI: 10.1080/16168658.2021.2019430
Saman Forouzandeh, M. Rostami, Kamal Berahmand
Recommender systems have been pervasively applied as a technique of suggesting travel recommendations to tourists. Actually, recommendation systems significantly contribute to the decision-making process of tourists. A new approach of recommendation systems in the tourism industry by a combination of the Artificial Bee Colony (ABC) algorithm and Fuzzy TOPSIS is proposed in the present paper. A multi-criteria decision-making method called the Techniques for Order of Preference by Similarity to Ideal Solution (TOPSIS) has been applied for the purpose of optimizing the system. Data were gathered through a 1015 online questionnaire on the Facebook social media site. In the first stage, the TOPSIS model defines a positive ideal solution in the form of a matrix with four columns, which indicates factors that get involved in this study. In the second stage, the ABC algorithm starts to search amongst destinations and recommends the best tourist spot to users.
{"title":"A Hybrid Method for Recommendation Systems based on Tourism with an Evolutionary Algorithm and Topsis Model","authors":"Saman Forouzandeh, M. Rostami, Kamal Berahmand","doi":"10.1080/16168658.2021.2019430","DOIUrl":"https://doi.org/10.1080/16168658.2021.2019430","url":null,"abstract":"Recommender systems have been pervasively applied as a technique of suggesting travel recommendations to tourists. Actually, recommendation systems significantly contribute to the decision-making process of tourists. A new approach of recommendation systems in the tourism industry by a combination of the Artificial Bee Colony (ABC) algorithm and Fuzzy TOPSIS is proposed in the present paper. A multi-criteria decision-making method called the Techniques for Order of Preference by Similarity to Ideal Solution (TOPSIS) has been applied for the purpose of optimizing the system. Data were gathered through a 1015 online questionnaire on the Facebook social media site. In the first stage, the TOPSIS model defines a positive ideal solution in the form of a matrix with four columns, which indicates factors that get involved in this study. In the second stage, the ABC algorithm starts to search amongst destinations and recommends the best tourist spot to users.","PeriodicalId":37623,"journal":{"name":"Fuzzy Information and Engineering","volume":"70 1","pages":"26 - 50"},"PeriodicalIF":1.2,"publicationDate":"2022-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84422188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-28DOI: 10.1080/16168658.2021.2019432
Farah Kamil, Mohammed Yasser Moghrabiah
The investigation into mobile robot navigation under uncertain dynamic environments is of great significance. This paper seeks to solve the current problems which are the difficulty to plan in indeterminate ever-changing environments, the problem of optimality, failure in complex situations, and the problem of predicting the obstacle velocity vector. The objective of this study is to propose a multilayer decision-based fuzzy logic model to find the solution for robot navigation through a safe path while preventing any types of barriers and to understand the non-collision mobile robots’ movement in an unknown dynamic environment. In this study, the prediction and priority rules of a multilayer decision are used by the fuzzy logic controller to improve the quality of the next position with regard to its path length, safety, and runtime. The results of comparison studies revealed a considerable improvement in failure rate and path length. Outcomes show that the suggested method displays attractive features, for instance, great stability, great optimality, zero failure rates, and low running time. The average path length for all test environments is 13.11 with 0.47 a standard deviation that provides 89% of an average optimality rate. The average running time is about 5.31 s with a 0.25 standard deviation.
{"title":"Multilayer Decision-Based Fuzzy Logic Model to Navigate Mobile Robot in Unknown Dynamic Environments","authors":"Farah Kamil, Mohammed Yasser Moghrabiah","doi":"10.1080/16168658.2021.2019432","DOIUrl":"https://doi.org/10.1080/16168658.2021.2019432","url":null,"abstract":"The investigation into mobile robot navigation under uncertain dynamic environments is of great significance. This paper seeks to solve the current problems which are the difficulty to plan in indeterminate ever-changing environments, the problem of optimality, failure in complex situations, and the problem of predicting the obstacle velocity vector. The objective of this study is to propose a multilayer decision-based fuzzy logic model to find the solution for robot navigation through a safe path while preventing any types of barriers and to understand the non-collision mobile robots’ movement in an unknown dynamic environment. In this study, the prediction and priority rules of a multilayer decision are used by the fuzzy logic controller to improve the quality of the next position with regard to its path length, safety, and runtime. The results of comparison studies revealed a considerable improvement in failure rate and path length. Outcomes show that the suggested method displays attractive features, for instance, great stability, great optimality, zero failure rates, and low running time. The average path length for all test environments is 13.11 with 0.47 a standard deviation that provides 89% of an average optimality rate. The average running time is about 5.31 s with a 0.25 standard deviation.","PeriodicalId":37623,"journal":{"name":"Fuzzy Information and Engineering","volume":"31 2 1","pages":"51 - 73"},"PeriodicalIF":1.2,"publicationDate":"2021-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74327154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-27DOI: 10.1080/16168658.2021.2019434
M. Niksirat
One of the most important problems in network design applications is the hub location problem, which is an extension of the facility location problem. The purpose of the problem is to select the least hub nodes from the available nodes so by establishing faster connections between hub nodes, the cost of transferring the entire network traffic is minimised. To deal with uncertainty and hesitation, the traffic amount between origin and destination nodes, the transfer cost, and the cost of establishing hub nodes are considered to be trapezoidal intuitionistic fuzzy numbers. The problem is formulated, and a new approach and a linearisation technique are shown to transform the Intuitionistic Fuzzy Hub Location Problem into a classical one. The transformed problem is solved using integer linear programming algorithms. The feasibility and efficiency of the obtained solutions applied to some airline passenger distribution problem applications are illustrated.
{"title":"Intuitionistic Fuzzy Hub Location Problems: Model and Solution Approach","authors":"M. Niksirat","doi":"10.1080/16168658.2021.2019434","DOIUrl":"https://doi.org/10.1080/16168658.2021.2019434","url":null,"abstract":"One of the most important problems in network design applications is the hub location problem, which is an extension of the facility location problem. The purpose of the problem is to select the least hub nodes from the available nodes so by establishing faster connections between hub nodes, the cost of transferring the entire network traffic is minimised. To deal with uncertainty and hesitation, the traffic amount between origin and destination nodes, the transfer cost, and the cost of establishing hub nodes are considered to be trapezoidal intuitionistic fuzzy numbers. The problem is formulated, and a new approach and a linearisation technique are shown to transform the Intuitionistic Fuzzy Hub Location Problem into a classical one. The transformed problem is solved using integer linear programming algorithms. The feasibility and efficiency of the obtained solutions applied to some airline passenger distribution problem applications are illustrated.","PeriodicalId":37623,"journal":{"name":"Fuzzy Information and Engineering","volume":"28 1","pages":"74 - 83"},"PeriodicalIF":1.2,"publicationDate":"2021-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90083866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-02DOI: 10.1080/16168658.2021.2002664
M. Nabipour, S. H. Nasseri, Elnaz Tavakoli Saber
Summary: This study examines the impacts of the built environment on pedestrian urban travels using a fuzzy AHP approach, by taking into account fifteen different variables based on three criteria: network design, environment, and safety. We gathered data from academic and industry experts using a fuzzy-based pairwise comparative survey. Advantage: We adopt two methods for selecting high-priority variables. The average value of cumulative weights, which prioritise variables with a weight greater than the average value, and a variation weights values analysis that divides variables into three groups as high, medium, and low priority depending on the weight pattern slope’s breaking points. The findings indicate that the weights variation approach is more effective. Limit: Because the survey statistical population comprised both academic and industrial experts, a significant amount of effort was spent identifying qualified candidates and gathering the necessary data. Results: The results prioritise effective variables including level of stress, lighting, obstacles on sidewalks, width of sidewalk, sidewalk surface quality, pedestrian bridges, cleanness and density of green areas, access to public transportation, intersection traffic controls, and walking utilities. Furthermore, the findings show that by growing policies on the variables of high and medium priority, up to 68 percent of the objective function can be achieved pedestrian urban commuting will significantly improve.
{"title":"Comparison and Evaluation of Built Environment Factors for Developing Pedestrian Urban Travels","authors":"M. Nabipour, S. H. Nasseri, Elnaz Tavakoli Saber","doi":"10.1080/16168658.2021.2002664","DOIUrl":"https://doi.org/10.1080/16168658.2021.2002664","url":null,"abstract":"Summary: This study examines the impacts of the built environment on pedestrian urban travels using a fuzzy AHP approach, by taking into account fifteen different variables based on three criteria: network design, environment, and safety. We gathered data from academic and industry experts using a fuzzy-based pairwise comparative survey. Advantage: We adopt two methods for selecting high-priority variables. The average value of cumulative weights, which prioritise variables with a weight greater than the average value, and a variation weights values analysis that divides variables into three groups as high, medium, and low priority depending on the weight pattern slope’s breaking points. The findings indicate that the weights variation approach is more effective. Limit: Because the survey statistical population comprised both academic and industrial experts, a significant amount of effort was spent identifying qualified candidates and gathering the necessary data. Results: The results prioritise effective variables including level of stress, lighting, obstacles on sidewalks, width of sidewalk, sidewalk surface quality, pedestrian bridges, cleanness and density of green areas, access to public transportation, intersection traffic controls, and walking utilities. Furthermore, the findings show that by growing policies on the variables of high and medium priority, up to 68 percent of the objective function can be achieved pedestrian urban commuting will significantly improve.","PeriodicalId":37623,"journal":{"name":"Fuzzy Information and Engineering","volume":"25 1","pages":"505 - 521"},"PeriodicalIF":1.2,"publicationDate":"2021-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86170197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-02DOI: 10.1080/16168658.2021.1997444
N. Ajmal, I. Jahan, B. Davvaz
This paper is in continuation of our previous works. In this paper, we study solvable L-subgroups of an L-group and establish a level subset characterisation for the same. Then, this level subset characterisation has been used to describe solvability of L-subgroups with the help of the notions of normal and subinvariant series of L-subgroups. Moreover, the concept of supersolvable L-subgroups of an L-group has been introduced. It has been established that supersolvable L-groups are closed under the formation of subgroups. Also, commutator L-subgroup of a supersolvable L-subgroup is shown to be nilpotent. In the last, we extend Zassenhaus Lemma to L-setting and utilise it to establish a version of Schreier Refinement Theorem in L-group Theory.
{"title":"Solvability, Supersolvability and Schreier Refinement Theorem for L-Subgroups","authors":"N. Ajmal, I. Jahan, B. Davvaz","doi":"10.1080/16168658.2021.1997444","DOIUrl":"https://doi.org/10.1080/16168658.2021.1997444","url":null,"abstract":"This paper is in continuation of our previous works. In this paper, we study solvable L-subgroups of an L-group and establish a level subset characterisation for the same. Then, this level subset characterisation has been used to describe solvability of L-subgroups with the help of the notions of normal and subinvariant series of L-subgroups. Moreover, the concept of supersolvable L-subgroups of an L-group has been introduced. It has been established that supersolvable L-groups are closed under the formation of subgroups. Also, commutator L-subgroup of a supersolvable L-subgroup is shown to be nilpotent. In the last, we extend Zassenhaus Lemma to L-setting and utilise it to establish a version of Schreier Refinement Theorem in L-group Theory.","PeriodicalId":37623,"journal":{"name":"Fuzzy Information and Engineering","volume":"145 1","pages":"470 - 496"},"PeriodicalIF":1.2,"publicationDate":"2021-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86216410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-02DOI: 10.1080/16168658.2021.2002544
S. Rivaz, Z. Saeidi
In the present paper, a multiobjective linear programming problem under uncertainty, particularly when parameters are given in interval forms, is investigated. In this case, it is assumed that objective coefficients and constraints parameters have arrived in interval numbers. Considering a suitable order relation for interval numbers, a solution procedure for dealing with such a problem is developed. A numerical example is provided to illustrate the efficiency of the solution procedure.
{"title":"Solving Multiobjective Linear Programming Problems with Interval Parameters","authors":"S. Rivaz, Z. Saeidi","doi":"10.1080/16168658.2021.2002544","DOIUrl":"https://doi.org/10.1080/16168658.2021.2002544","url":null,"abstract":"In the present paper, a multiobjective linear programming problem under uncertainty, particularly when parameters are given in interval forms, is investigated. In this case, it is assumed that objective coefficients and constraints parameters have arrived in interval numbers. Considering a suitable order relation for interval numbers, a solution procedure for dealing with such a problem is developed. A numerical example is provided to illustrate the efficiency of the solution procedure.","PeriodicalId":37623,"journal":{"name":"Fuzzy Information and Engineering","volume":"10 1","pages":"497 - 504"},"PeriodicalIF":1.2,"publicationDate":"2021-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86961044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-02DOI: 10.1080/16168658.2021.2002665
T. Soumya, M. Sabu
The Probabilistic Rough Sets (PRS) theory determines the certainty of an object's inclusion into a class, resulting in the division of the entire data set into three regions under a concept. These regions, namely the positive, negative and boundary regions, are generated using an evaluation function and threshold values. The threshold optimisation and the construction and interpretation of an evaluation function offer various methods in the background. Even though most of the methods in the PRS follow an iterative strategy, they lack a common framework, usually affecting the comparison and overall performance evaluation among these methods. This proposed work aims to minimise the uncertainty in three regions via optimising the thresholds using the Artificial Bee Colony (ABC) algorithm. The ABC algorithm is adapted to generate a common framework that results in different optimal pairs of thresholds with a minimum number of iterations. By considering the probabilistic information about an equivalence class structure, we compare the results obtained from the proposed approach with the state-of-the-art methods like Information-Theoretic Rough Sets, Game-Theoretic Rough sets and Genetic Algorithm-based optimisation. The results reveal that the proposed algorithm outperforms existing techniques and leads to a superior method for threshold optimisation in the PRS.
{"title":"Optimisation of Thresholds in Probabilistic Rough Sets with Artificial Bee Colony Algorithm","authors":"T. Soumya, M. Sabu","doi":"10.1080/16168658.2021.2002665","DOIUrl":"https://doi.org/10.1080/16168658.2021.2002665","url":null,"abstract":"The Probabilistic Rough Sets (PRS) theory determines the certainty of an object's inclusion into a class, resulting in the division of the entire data set into three regions under a concept. These regions, namely the positive, negative and boundary regions, are generated using an evaluation function and threshold values. The threshold optimisation and the construction and interpretation of an evaluation function offer various methods in the background. Even though most of the methods in the PRS follow an iterative strategy, they lack a common framework, usually affecting the comparison and overall performance evaluation among these methods. This proposed work aims to minimise the uncertainty in three regions via optimising the thresholds using the Artificial Bee Colony (ABC) algorithm. The ABC algorithm is adapted to generate a common framework that results in different optimal pairs of thresholds with a minimum number of iterations. By considering the probabilistic information about an equivalence class structure, we compare the results obtained from the proposed approach with the state-of-the-art methods like Information-Theoretic Rough Sets, Game-Theoretic Rough sets and Genetic Algorithm-based optimisation. The results reveal that the proposed algorithm outperforms existing techniques and leads to a superior method for threshold optimisation in the PRS.","PeriodicalId":37623,"journal":{"name":"Fuzzy Information and Engineering","volume":"34 1","pages":"522 - 539"},"PeriodicalIF":1.2,"publicationDate":"2021-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77537884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-02DOI: 10.1080/16168658.2021.1978803
Samane Sharif, M. Akbarzadeh-T.
INTRODUCTION: With the growing size, complexity, and distributivity of databases, efficiency and scalability have become highly desirable attributes of data mining algorithms in decision support systems. OBJECTIVES: This study aims for a computational framework for clinical decision support systems that can handle inconsistent dataset while also being interpretable and scalable. METHODS: This paper proposes a Distributed Probabilistic Fuzzy Rule Mining (DPFRM) algorithm that extracts probabilistic fuzzy rules from numerical data using a self-organizing multi-agent approach. This agent-based method provides better scalability and fewer rules through agent interactions and rule-sharing. RESULTS: The performance of the proposed approach is investigated on several UCI medical datasets. The DPFRM is also used for predicting the mortality rate of burn patients. Statistical analysis confirms that the DPFRM significantly improves burn mortality prediction by at least 3%. Also, the training time is improved by 17% if implemented by a parallel computer. However, this speedup decreases with increased distributivity, due to the added communication overhead. CONCLUSION: The proposed approach can improve the accuracy of decision making by better handling of inconsistencies within the datasets. Furthermore, noise sensitivity analysis demonstrates that the DPFRM deteriorates more robustly as the noise levels increase.
{"title":"Distributed Probabilistic Fuzzy Rule Mining for Clinical Decision Making","authors":"Samane Sharif, M. Akbarzadeh-T.","doi":"10.1080/16168658.2021.1978803","DOIUrl":"https://doi.org/10.1080/16168658.2021.1978803","url":null,"abstract":"INTRODUCTION: With the growing size, complexity, and distributivity of databases, efficiency and scalability have become highly desirable attributes of data mining algorithms in decision support systems. OBJECTIVES: This study aims for a computational framework for clinical decision support systems that can handle inconsistent dataset while also being interpretable and scalable. METHODS: This paper proposes a Distributed Probabilistic Fuzzy Rule Mining (DPFRM) algorithm that extracts probabilistic fuzzy rules from numerical data using a self-organizing multi-agent approach. This agent-based method provides better scalability and fewer rules through agent interactions and rule-sharing. RESULTS: The performance of the proposed approach is investigated on several UCI medical datasets. The DPFRM is also used for predicting the mortality rate of burn patients. Statistical analysis confirms that the DPFRM significantly improves burn mortality prediction by at least 3%. Also, the training time is improved by 17% if implemented by a parallel computer. However, this speedup decreases with increased distributivity, due to the added communication overhead. CONCLUSION: The proposed approach can improve the accuracy of decision making by better handling of inconsistencies within the datasets. Furthermore, noise sensitivity analysis demonstrates that the DPFRM deteriorates more robustly as the noise levels increase.","PeriodicalId":37623,"journal":{"name":"Fuzzy Information and Engineering","volume":"30 1","pages":"436 - 459"},"PeriodicalIF":1.2,"publicationDate":"2021-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81181813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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