Pub Date : 2012-09-11DOI: 10.1142/S0218488512400144
David García, Antonio González, Raúl Pérez
In system identification process often a predetermined set of features is used. However, in many cases it is difficult to know a priori whether the selected features were really the more appropriate ones. This is the reason why the feature construction techniques have been very interesting in many applications. Thus, the current proposal introduces the use of these techniques in order to improve the description of fuzzy rule-based systems. In particular, the idea is to include feature construction in a genetic learning algorithm. The construction of attributes in this study will be restricted to the inclusion of functions defined on the initial attributes of the system. Since the number of functions and the number of attributes can be very large, a filter model, based on the use of information measures, is introduced. In this way, the genetic algorithm only needs to explore the particular new features that may be of greater interest to the final identification of the system. In order to manage the knowledge provided by the new attributes based on the use of functions we propose a new model of rule by extending a basic learning fuzzy rule-based model. Finally, we show the experimental study associated with this work.
{"title":"A FILTER PROPOSAL FOR INCLUDING FEATURE CONSTRUCTION IN A GENETIC LEARNING ALGORITHM","authors":"David García, Antonio González, Raúl Pérez","doi":"10.1142/S0218488512400144","DOIUrl":"https://doi.org/10.1142/S0218488512400144","url":null,"abstract":"In system identification process often a predetermined set of features is used. However, in many cases it is difficult to know a priori whether the selected features were really the more appropriate ones. This is the reason why the feature construction techniques have been very interesting in many applications. Thus, the current proposal introduces the use of these techniques in order to improve the description of fuzzy rule-based systems. In particular, the idea is to include feature construction in a genetic learning algorithm. The construction of attributes in this study will be restricted to the inclusion of functions defined on the initial attributes of the system. Since the number of functions and the number of attributes can be very large, a filter model, based on the use of information measures, is introduced. In this way, the genetic algorithm only needs to explore the particular new features that may be of greater interest to the final identification of the system. In order to manage the knowledge provided by the new attributes based on the use of functions we propose a new model of rule by extending a basic learning fuzzy rule-based model. Finally, we show the experimental study associated with this work.","PeriodicalId":50283,"journal":{"name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","volume":"121 1","pages":"31-49"},"PeriodicalIF":1.5,"publicationDate":"2012-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90905173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-09-11DOI: 10.1142/S021848851240017X
K. Balázs, L. Kóczy
In this paper a family of new methods are proposed for constructing hierarchical-interpolative fuzzy rule bases in the frame of a fuzzy rule based supervised machine learning system modeling black box systems defined by input-output pairs. The resulting hierarchical rule base is constructed by using structure building pure evolutionary and memetic techniques, namely, Genetic and Bacterial Programming Algorithms and their memetic variants containing local search steps. Applying hierarchical-interpolative fuzzy rule bases is a rather efficient way of reducing the complexity of knowledge bases, whereas evolutionary methods (including memetic techniques) ensure a relatively fast convergence in the learning process. As it is presented in the paper, by applying a newly proposed representation schema these approaches can be combined to form hierarchical-interpolative machine learning systems.
{"title":"HIERARCHICAL-INTERPOLATIVE FUZZY SYSTEM CONSTRUCTION BY GENETIC AND BACTERIAL MEMETIC PROGRAMMING APPROACHES","authors":"K. Balázs, L. Kóczy","doi":"10.1142/S021848851240017X","DOIUrl":"https://doi.org/10.1142/S021848851240017X","url":null,"abstract":"In this paper a family of new methods are proposed for constructing hierarchical-interpolative fuzzy rule bases in the frame of a fuzzy rule based supervised machine learning system modeling black box systems defined by input-output pairs. The resulting hierarchical rule base is constructed by using structure building pure evolutionary and memetic techniques, namely, Genetic and Bacterial Programming Algorithms and their memetic variants containing local search steps. Applying hierarchical-interpolative fuzzy rule bases is a rather efficient way of reducing the complexity of knowledge bases, whereas evolutionary methods (including memetic techniques) ensure a relatively fast convergence in the learning process. As it is presented in the paper, by applying a newly proposed representation schema these approaches can be combined to form hierarchical-interpolative machine learning systems.","PeriodicalId":50283,"journal":{"name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","volume":"4 1","pages":"105-131"},"PeriodicalIF":1.5,"publicationDate":"2012-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72942078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-09-11DOI: 10.1142/S0218488512400181
Fathi Gasir, Keeley A. Crockett, Z. Bandar
Fuzzy decision forests aim to improve the predictive power of single fuzzy decision trees by allowing multiple views of the same domain to be modelled. Such forests have been successfully created for classification problems where the outcome field is discrete; however predicting a continuous output value is more challenging in combining the output from multiple fuzzy decision trees. This paper presents a new approach to creating fuzzy regression tree forests based upon the induction of multiple fuzzy regression decision trees from one training sample, where each tree will represent a different view of the data domain. The singular fuzzy regression trees are induced using a proven algorithm known as Elgasir which fuzzifies crisp CHAID decision trees using trapezoidal membership functions for fuzzification and applies Takagi-Sugeno inference to obtain the final predicted values. A modified version of Artificial Immune System Network model (opt-aiNet) is then used for the simultaneous optimization of the membership functions across all trees within the forest. A strength of the proposed method is that data does not require fuzzification before forest induction this reducing pre-processing time and the need for subjective human experts. Five problem sets from the UCI repository and KEEL repository are used to evaluate the approach. The experimental results have shown that fuzzy regression tree forests reduce the error rate compared with single fuzzy regression trees.
{"title":"INDUCING FUZZY REGRESSION TREE FORESTS USING ARTIFICIAL IMMUNE SYSTEMS","authors":"Fathi Gasir, Keeley A. Crockett, Z. Bandar","doi":"10.1142/S0218488512400181","DOIUrl":"https://doi.org/10.1142/S0218488512400181","url":null,"abstract":"Fuzzy decision forests aim to improve the predictive power of single fuzzy decision trees by allowing multiple views of the same domain to be modelled. Such forests have been successfully created for classification problems where the outcome field is discrete; however predicting a continuous output value is more challenging in combining the output from multiple fuzzy decision trees. This paper presents a new approach to creating fuzzy regression tree forests based upon the induction of multiple fuzzy regression decision trees from one training sample, where each tree will represent a different view of the data domain. The singular fuzzy regression trees are induced using a proven algorithm known as Elgasir which fuzzifies crisp CHAID decision trees using trapezoidal membership functions for fuzzification and applies Takagi-Sugeno inference to obtain the final predicted values. A modified version of Artificial Immune System Network model (opt-aiNet) is then used for the simultaneous optimization of the membership functions across all trees within the forest. A strength of the proposed method is that data does not require fuzzification before forest induction this reducing pre-processing time and the need for subjective human experts. Five problem sets from the UCI repository and KEEL repository are used to evaluate the approach. The experimental results have shown that fuzzy regression tree forests reduce the error rate compared with single fuzzy regression trees.","PeriodicalId":50283,"journal":{"name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","volume":"185 1","pages":"133-157"},"PeriodicalIF":1.5,"publicationDate":"2012-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72791980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-07-09DOI: 10.1142/S0218488512400089
Wenjiang Li, Jun Liu, Hui Wang, A. Calzada, Rosa M. Rodríguez, L. Martínez
This paper focuses on an inference methodology based on a belief linguistic rule base (B-LRB) for qualitative decision support. It is termed 'linguistic rule-base' instead of 'fuzzy rule-base' because the use of membership functions associated with the linguistic terms are unnecessary or do not play a key role. The features of B-LRB, the ways to generate a B-LRB, and the inference procedure based on B-LRB are specified, along with an illustrate example applied to evaluate consumer trustworthiness in Internet marketing to show how it works, its applicability and feasibility.
{"title":"A qualitative decision making model based on belief linguistic rule based inference methodology","authors":"Wenjiang Li, Jun Liu, Hui Wang, A. Calzada, Rosa M. Rodríguez, L. Martínez","doi":"10.1142/S0218488512400089","DOIUrl":"https://doi.org/10.1142/S0218488512400089","url":null,"abstract":"This paper focuses on an inference methodology based on a belief linguistic rule base (B-LRB) for qualitative decision support. It is termed 'linguistic rule-base' instead of 'fuzzy rule-base' because the use of membership functions associated with the linguistic terms are unnecessary or do not play a key role. The features of B-LRB, the ways to generate a B-LRB, and the inference procedure based on B-LRB are specified, along with an illustrate example applied to evaluate consumer trustworthiness in Internet marketing to show how it works, its applicability and feasibility.","PeriodicalId":50283,"journal":{"name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","volume":"17 1","pages":"105-118"},"PeriodicalIF":1.5,"publicationDate":"2012-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73416262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-07-09DOI: 10.1142/S0218488512400053
I. Sari, H. Behret, C. Kahraman
The urban rail system in Istanbul carries in total more than 700.000 passengers per a day on different types of lines which require well organized risk governance. This paper evaluates the urban rail systems in Istanbul under different risk factors using Fuzzy Analytic Hierarchy Process (FAHP) to uncover the critical risk criteria of these systems and to make a multi-criteria evaluation of existing rail systems for the assignment of the scarce resources. Linguistic variables are used in the pairwise comparisons of criteria and alternatives. The risk factors considered are regional criticality, line characteristics, line safety and station structure. The evaluation results imply that the most risky critical urban rail system in Istanbul is the subway line from Sishane to Darussafaka.
{"title":"RISK GOVERNANCE OF URBAN RAIL SYSTEMS USING FUZZY AHP: THE CASE OF ISTANBUL","authors":"I. Sari, H. Behret, C. Kahraman","doi":"10.1142/S0218488512400053","DOIUrl":"https://doi.org/10.1142/S0218488512400053","url":null,"abstract":"The urban rail system in Istanbul carries in total more than 700.000 passengers per a day on different types of lines which require well organized risk governance. This paper evaluates the urban rail systems in Istanbul under different risk factors using Fuzzy Analytic Hierarchy Process (FAHP) to uncover the critical risk criteria of these systems and to make a multi-criteria evaluation of existing rail systems for the assignment of the scarce resources. Linguistic variables are used in the pairwise comparisons of criteria and alternatives. The risk factors considered are regional criticality, line characteristics, line safety and station structure. The evaluation results imply that the most risky critical urban rail system in Istanbul is the subway line from Sishane to Darussafaka.","PeriodicalId":50283,"journal":{"name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","volume":"179 3 1","pages":"67-79"},"PeriodicalIF":1.5,"publicationDate":"2012-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72871964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-07-09DOI: 10.1142/S0218488512400016
D. Ruan, F. Hardeman, L. Mkrtchyan
Safety Culture describes how safety issues are managed within an enterprise. How to make safety culture strong and sustainable? How to be sure that safety is a prime responsibility or main focus for all types of activity? How to improve safety culture and how to identify the most vulnerable issues of safety culture? These are important questions for safety culture. Huge amount of studies focus on identifying and building the hierarchy of the main indicators of safety culture. However, there are only few methods to assess an organization's safety culture and those methods are often straightforward. In this paper we describe a novel approach for safety culture assessment by using Belief Degree-Distributed Fuzzy Cognitive Maps (BDD-FCMs). Cognitive maps were initially presented for graphical representation of uncertain causal reasoning. Later Kosko suggested Fuzzy Cognitive Maps FCMs in which users freely express their opinions in linguistic terms instead of crisp numbers. However, it is not always easy to assign some linguistic term to a causal link. By using BDD-FCMs, causal links are expressed by belief structures which enable getting the links evaluations with distributions over the linguistic terms. In addition, we propose a general framework to construct BDD-FCMs by directly using belief structures or other types of structures such as intervals, linguistic terms, or crisp numbers. The proposed framework provides a more flexible tool for causal reasoning as it handles different structures to evaluate causal links.
{"title":"A NOVEL APPROACH FOR SAFETY CULTURE ASSESSMENT","authors":"D. Ruan, F. Hardeman, L. Mkrtchyan","doi":"10.1142/S0218488512400016","DOIUrl":"https://doi.org/10.1142/S0218488512400016","url":null,"abstract":"Safety Culture describes how safety issues are managed within an enterprise. How to make safety culture strong and sustainable? How to be sure that safety is a prime responsibility or main focus for all types of activity? How to improve safety culture and how to identify the most vulnerable issues of safety culture? These are important questions for safety culture. Huge amount of studies focus on identifying and building the hierarchy of the main indicators of safety culture. However, there are only few methods to assess an organization's safety culture and those methods are often straightforward. In this paper we describe a novel approach for safety culture assessment by using Belief Degree-Distributed Fuzzy Cognitive Maps (BDD-FCMs). Cognitive maps were initially presented for graphical representation of uncertain causal reasoning. Later Kosko suggested Fuzzy Cognitive Maps FCMs in which users freely express their opinions in linguistic terms instead of crisp numbers. However, it is not always easy to assign some linguistic term to a causal link. By using BDD-FCMs, causal links are expressed by belief structures which enable getting the links evaluations with distributions over the linguistic terms. In addition, we propose a general framework to construct BDD-FCMs by directly using belief structures or other types of structures such as intervals, linguistic terms, or crisp numbers. The proposed framework provides a more flexible tool for causal reasoning as it handles different structures to evaluate causal links.","PeriodicalId":50283,"journal":{"name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","volume":"125 1","pages":"1-15"},"PeriodicalIF":1.5,"publicationDate":"2012-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74762614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-07-09DOI: 10.1142/S0218488512400065
V. López, G. Miñana
Performance, reliability and safety are relevant factors when analyzing or designing a computer system. Many studies about on performance are based on monitoring and analyzing data from a computer system. One of the most useful pieces of data is the Load Average (LA) that which shows the load average of the system in the last minute, the sequence of in the last five minutes and the sequence of in the last fifteen last minutes. There are a lot ofmany studies of the system performance based on the load average. This is shown by mean means of monitoring the commands of the operative system, but sometimes they are sometimes difficult to understand and far of removed from human intuition. The aim of this paper is to show demonstrate a new procedure that allows us to determine the stability of a computer system from a list of load average sample data. The idea is shown as an algorithm based in statistic analysis, the aggregation of information and its formal specification. The result is an evaluation of the stability of the load and the computer system by monitoring but without adding any overhead to the system. In addition, the procedure can be used as a software monitor for risk prevention of on any vulnerable system.
{"title":"MODELING THE STABILITY OF A COMPUTER SYSTEM","authors":"V. López, G. Miñana","doi":"10.1142/S0218488512400065","DOIUrl":"https://doi.org/10.1142/S0218488512400065","url":null,"abstract":"Performance, reliability and safety are relevant factors when analyzing or designing a computer system. Many studies about on performance are based on monitoring and analyzing data from a computer system. One of the most useful pieces of data is the Load Average (LA) that which shows the load average of the system in the last minute, the sequence of in the last five minutes and the sequence of in the last fifteen last minutes. There are a lot ofmany studies of the system performance based on the load average. This is shown by mean means of monitoring the commands of the operative system, but sometimes they are sometimes difficult to understand and far of removed from human intuition. The aim of this paper is to show demonstrate a new procedure that allows us to determine the stability of a computer system from a list of load average sample data. The idea is shown as an algorithm based in statistic analysis, the aggregation of information and its formal specification. The result is an evaluation of the stability of the load and the computer system by monitoring but without adding any overhead to the system. In addition, the procedure can be used as a software monitor for risk prevention of on any vulnerable system.","PeriodicalId":50283,"journal":{"name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","volume":"24 1","pages":"81-90"},"PeriodicalIF":1.5,"publicationDate":"2012-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74619222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-07-09DOI: 10.1142/S0218488512400028
D. Tang, Jianbo Yang, D. Bamford, Dongling Xu, M. Waugh, J. Bamford, Shulian Zhang
Enterprise Risk Management (ERM) is a framework that is used by large organizations to manage risk as a whole. The key difference between ERM and traditional risk management is that in the latter risks are managed individually, whilst the former requires the aggregation of risks to facilitate risk management. However, current methods for risk aggregation have various limitations when applied under the context of ERM, such as the requirement for accurate and complete information about risk factors, the inability to handle different kinds of uncertainty which are inevitable during the risk aggregation process, and so on. Due to its unique advantages in accommodating different forms of both complete and incomplete information and handling different kinds of uncertainty, the Evidential Reasoning (ER) approach together with its implementation entitled Intelligent Decision System (IDS) is introduced in this paper for risk aggregation in ERM to overcome the limitations and to provide a comprehensive analysis for risk management based on the aggregation result. To demonstrate the applicability of the ER approach and IDS in ERM, a case study is analyzed in detail regarding risk aggregation and risk management for a health care organization in North England.
{"title":"The evidential reasoning approach for risk management in large enterprises","authors":"D. Tang, Jianbo Yang, D. Bamford, Dongling Xu, M. Waugh, J. Bamford, Shulian Zhang","doi":"10.1142/S0218488512400028","DOIUrl":"https://doi.org/10.1142/S0218488512400028","url":null,"abstract":"Enterprise Risk Management (ERM) is a framework that is used by large organizations to manage risk as a whole. The key difference between ERM and traditional risk management is that in the latter risks are managed individually, whilst the former requires the aggregation of risks to facilitate risk management. However, current methods for risk aggregation have various limitations when applied under the context of ERM, such as the requirement for accurate and complete information about risk factors, the inability to handle different kinds of uncertainty which are inevitable during the risk aggregation process, and so on. Due to its unique advantages in accommodating different forms of both complete and incomplete information and handling different kinds of uncertainty, the Evidential Reasoning (ER) approach together with its implementation entitled Intelligent Decision System (IDS) is introduced in this paper for risk aggregation in ERM to overcome the limitations and to provide a comprehensive analysis for risk management based on the aggregation result. To demonstrate the applicability of the ER approach and IDS in ERM, a case study is analyzed in detail regarding risk aggregation and risk management for a health care organization in North England.","PeriodicalId":50283,"journal":{"name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","volume":"31 1","pages":"17-30"},"PeriodicalIF":1.5,"publicationDate":"2012-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74944031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-07-09DOI: 10.1142/S0218488512400119
Shaojie Qiao, Tianrui Li, Yan Yang, Christopher C. Yang
Identifying key members from web-based social networks assists in assessing the risk of criminal network formation. To manage the uncertainty in complex web-based social networks, we first formally defined the binary relation and uncertainty of pages in web-based social networks. Secondly, we proposed an effective algorithm for Mining Key member from uncertain web-based social networks, called MiKey, by integrating uncertainty of pages into three centrality measures including degree, betweenness, and closeness. MiKey takes into a full consideration of the uncertainty in web-based social networks by computing the transition probability from one page to another. Furthermore, we briefly introduced the approach of calculating the k-order transition matrix of pages. Finally, we conducted experiments on real web data and the results show that MiKey is effective in discovering key pages from web-based social networks with less time deficiency than the centrality measures based algorithm.
{"title":"MANAGING UNCERTAINTY IN WEB-BASED SOCIAL NETWORKS","authors":"Shaojie Qiao, Tianrui Li, Yan Yang, Christopher C. Yang","doi":"10.1142/S0218488512400119","DOIUrl":"https://doi.org/10.1142/S0218488512400119","url":null,"abstract":"Identifying key members from web-based social networks assists in assessing the risk of criminal network formation. To manage the uncertainty in complex web-based social networks, we first formally defined the binary relation and uncertainty of pages in web-based social networks. Secondly, we proposed an effective algorithm for Mining Key member from uncertain web-based social networks, called MiKey, by integrating uncertainty of pages into three centrality measures including degree, betweenness, and closeness. MiKey takes into a full consideration of the uncertainty in web-based social networks by computing the transition probability from one page to another. Furthermore, we briefly introduced the approach of calculating the k-order transition matrix of pages. Finally, we conducted experiments on real web data and the results show that MiKey is effective in discovering key pages from web-based social networks with less time deficiency than the centrality measures based algorithm.","PeriodicalId":50283,"journal":{"name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","volume":"8 1","pages":"147-158"},"PeriodicalIF":1.5,"publicationDate":"2012-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74343731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：