Pub Date : 2007-07-23DOI: 10.1109/FUZZY.2007.4295536
Corrado Mencar, A. Consiglio, A. Fanelli
In this paper we present an approach for extracting interpretable information granules for classification. The approach, called DCγ (double clustering with genetic algorithms) is based on two clustering steps. The first step uses LVQ1 to identify cluster prototypes in the multidimensional data space so as to represent hidden relationships among data. In the second step a genetic algorithm is applied to the projections of these prototypes with the objective of finding a minimal number of fuzzy information granules that verify some interpretability constraints. The key feature of DCγ is the efficiency of the minimization process carried out in the second step. Experimental results on two medical diagnosis problems show the effectiveness of the proposed approach in terms of accuracy, interpretability and efficiency.
{"title":"DCγ : Interpretable Granulation of Data through GA-based Double Clustering","authors":"Corrado Mencar, A. Consiglio, A. Fanelli","doi":"10.1109/FUZZY.2007.4295536","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295536","url":null,"abstract":"In this paper we present an approach for extracting interpretable information granules for classification. The approach, called DCγ (double clustering with genetic algorithms) is based on two clustering steps. The first step uses LVQ1 to identify cluster prototypes in the multidimensional data space so as to represent hidden relationships among data. In the second step a genetic algorithm is applied to the projections of these prototypes with the objective of finding a minimal number of fuzzy information granules that verify some interpretability constraints. The key feature of DCγ is the efficiency of the minimization process carried out in the second step. Experimental results on two medical diagnosis problems show the effectiveness of the proposed approach in terms of accuracy, interpretability and efficiency.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134188221","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295502
E. Herrera-López, Bernardino Castillo, Jesús Ramírez, E. Ferreira
The purpose of this work is to design an exact fuzzy observer for a bioprocess switching between two different metabolic states. A fed-batch baker's yeast culture is modeled by two sub-models: a respiro-fermentative state with ethanol production and a respirative state with ethanol consumption. An exact fuzzy observer model using sector nonlinearity was built for both nonlinear models; the observer gains were designed using Linear Matrix Inequalities (LMI's). The observer dynamics shows a very good tracking behavior with respect of the states of the switching partial models. The observer premise variables depend on the state variables estimated by the fuzzy observer.
{"title":"Exact Fuzzy Observer for a Baker's Yeast Fed-Batch Fermentation Process","authors":"E. Herrera-López, Bernardino Castillo, Jesús Ramírez, E. Ferreira","doi":"10.1109/FUZZY.2007.4295502","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295502","url":null,"abstract":"The purpose of this work is to design an exact fuzzy observer for a bioprocess switching between two different metabolic states. A fed-batch baker's yeast culture is modeled by two sub-models: a respiro-fermentative state with ethanol production and a respirative state with ethanol consumption. An exact fuzzy observer model using sector nonlinearity was built for both nonlinear models; the observer gains were designed using Linear Matrix Inequalities (LMI's). The observer dynamics shows a very good tracking behavior with respect of the states of the switching partial models. The observer premise variables depend on the state variables estimated by the fuzzy observer.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"274 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134273089","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295433
Yueh-Hsiang Chen, Ru-Jen Chao
E-learning has gradually become more and more important nowadays due to its advantages: (1) learning courses without constraints of time and place through asynchronous distance learning (2) saving training cost for enterprises. Official organizations, schools, and businesses invest a lot of time, money, and efforts in e-learning. A variety of e-learning materials were generated under such situations. This study is to inspect the affecting factors of e-learning material and apply consistent fuzzy linguistic preference relations to evaluate these factors. The evaluation can offer the information of decision-making to the e-learning material designers, especially with the constraint of time and money.
{"title":"Applying Consistent Fuzzy Linguistic Preference Relations to Evaluation of E-Learning Material Design","authors":"Yueh-Hsiang Chen, Ru-Jen Chao","doi":"10.1109/FUZZY.2007.4295433","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295433","url":null,"abstract":"E-learning has gradually become more and more important nowadays due to its advantages: (1) learning courses without constraints of time and place through asynchronous distance learning (2) saving training cost for enterprises. Official organizations, schools, and businesses invest a lot of time, money, and efforts in e-learning. A variety of e-learning materials were generated under such situations. This study is to inspect the affecting factors of e-learning material and apply consistent fuzzy linguistic preference relations to evaluate these factors. The evaluation can offer the information of decision-making to the e-learning material designers, especially with the constraint of time and money.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"359 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133041850","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295550
A. Hajiha, J. Jassbi, S. Khanmohammadi
Choosing suitable individuals for different positions in an organization has always been one of the most important concerns of management scientists. Several mathematical models have been used to quantify the comparable merits of individuals for different jobs. These are generally developed in a deterministic environment and deal with precise data while evaluating the extent of "merit" falls under a fuzzy environment with completely nonlinear relations between merits and behavioral features of employers. This paper deals with assigning individuals to jobs through designing a fuzzy model as an expert decision support system. To design such model, experimental knowledge of experts is benefited to define the rules. The extreme conditions are used to facilitate and to increase the precisions of experts' judgments. Then through evaluating individuals' scores and applying the obtained results to the fuzzy rule base, fuzzy merits are obtained for different individuals. Finally the linear assignment technique is applied to use the defuzzified merits to achieve an optimal job assignment. An auto after sales services company is considered as a case study.
{"title":"A Fuzzy Expert Decision Support System for Job Assignment","authors":"A. Hajiha, J. Jassbi, S. Khanmohammadi","doi":"10.1109/FUZZY.2007.4295550","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295550","url":null,"abstract":"Choosing suitable individuals for different positions in an organization has always been one of the most important concerns of management scientists. Several mathematical models have been used to quantify the comparable merits of individuals for different jobs. These are generally developed in a deterministic environment and deal with precise data while evaluating the extent of \"merit\" falls under a fuzzy environment with completely nonlinear relations between merits and behavioral features of employers. This paper deals with assigning individuals to jobs through designing a fuzzy model as an expert decision support system. To design such model, experimental knowledge of experts is benefited to define the rules. The extreme conditions are used to facilitate and to increase the precisions of experts' judgments. Then through evaluating individuals' scores and applying the obtained results to the fuzzy rule base, fuzzy merits are obtained for different individuals. Finally the linear assignment technique is applied to use the defuzzified merits to achieve an optimal job assignment. An auto after sales services company is considered as a case study.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"79 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133089392","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295572
F. P. Romero, J. A. Olivas, P. J. Garcés
In this paper, a novel document filtering model in dynamic web repositories based on fuzzy deformable prototypes is presented. This model is based on fuzzy hierarchical categorization of documents. It defines an easy process to deal with the incoming documents and an efficient method to update their structure. The process is performed comparing the fuzzy prototypes of document cluster with the available information about documents contents. It exploits conceptual-based filtering criteria and category-based filtering techniques to deliver to the user an intelligent structure of the documents. Since filtering is a dynamic process, the knowledge base can update the hierarchy of existing documents. The clusters hierarchy can be easily and efficiently updated when new documents income on the repository by means of an inference method which is based on fuzzy deformable prototypes.
{"title":"Inference based on Fuzzy Deformable Prototypes for information filtering in dynamic web repositories","authors":"F. P. Romero, J. A. Olivas, P. J. Garcés","doi":"10.1109/FUZZY.2007.4295572","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295572","url":null,"abstract":"In this paper, a novel document filtering model in dynamic web repositories based on fuzzy deformable prototypes is presented. This model is based on fuzzy hierarchical categorization of documents. It defines an easy process to deal with the incoming documents and an efficient method to update their structure. The process is performed comparing the fuzzy prototypes of document cluster with the available information about documents contents. It exploits conceptual-based filtering criteria and category-based filtering techniques to deliver to the user an intelligent structure of the documents. Since filtering is a dynamic process, the knowledge base can update the hierarchy of existing documents. The clusters hierarchy can be easily and efficiently updated when new documents income on the repository by means of an inference method which is based on fuzzy deformable prototypes.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130131876","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295631
R. Giráldez, F. Divina, Beatriz Pontes, J. Aguilar-Ruiz
Biclustering techniques aim at extracting significant subsets of genes and conditions from microarray gene expression data. This kind of algorithms is mainly based on two key aspects: the way in which they deal with gene similarity across the experimental conditions, that determines the quality of biclusters; and the heuristic or search strategy used for exploring the search space. A measure that is often adopted for establishing the quality of biclusters is the mean squared residue. This measure has been successfully used in many approaches. However, it has been recently proven that the mean squared residue fails to recognize some kind of biclusters as quality biclusters, mainly due to the difficulty of detecting scaling patterns in data. In this work, we propose a novel measure for trying to overcome this drawback. This measure is based on the area between two curves. Such curves are built from the maximum and minimum standardized expression values exhibited for each experimental condition. In order to test the proposed measure, we have incorporated it into a multiobjective evolutionary algorithm. Experimental results confirm the effectiveness of our approach. The combination of the measure we propose with the mean squared residue yields results that would not have been obtained if only the mean squared residue had been used.
{"title":"Evolutionary Search of Biclusters by Minimal Intrafluctuation","authors":"R. Giráldez, F. Divina, Beatriz Pontes, J. Aguilar-Ruiz","doi":"10.1109/FUZZY.2007.4295631","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295631","url":null,"abstract":"Biclustering techniques aim at extracting significant subsets of genes and conditions from microarray gene expression data. This kind of algorithms is mainly based on two key aspects: the way in which they deal with gene similarity across the experimental conditions, that determines the quality of biclusters; and the heuristic or search strategy used for exploring the search space. A measure that is often adopted for establishing the quality of biclusters is the mean squared residue. This measure has been successfully used in many approaches. However, it has been recently proven that the mean squared residue fails to recognize some kind of biclusters as quality biclusters, mainly due to the difficulty of detecting scaling patterns in data. In this work, we propose a novel measure for trying to overcome this drawback. This measure is based on the area between two curves. Such curves are built from the maximum and minimum standardized expression values exhibited for each experimental condition. In order to test the proposed measure, we have incorporated it into a multiobjective evolutionary algorithm. Experimental results confirm the effectiveness of our approach. The combination of the measure we propose with the mean squared residue yields results that would not have been obtained if only the mean squared residue had been used.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131282177","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295505
V. Galdi, V. Loia, A. Piccolo, Mario Veniero
Nowadays, in Europe tunnel safety is a problem of great interest. Recent tragic events have led community and national politicians to start a process of legislation harmonization planning investments toward new technologies designed to improve tunnel safety. The purpose of this paper is to present an innovative system aiming at monitoring and steering vehicular flows nearby tunnels with high accidents risk rate. Based on the integration of IC, video, agent and soft computing technologies, the system's mission is to reduce the risk of accidents which occur inside tunnel through the adaptive generation of speed limits and information to the users approximating tunnels. Strict real-time requirements of the applicative context impose our goal being achieved through a parallel hierarchical fuzzy controller (HFC) implemented by means of a fuzzy control agents network (FCAN) allowing the system to gain higher performances. The fuzzy controller is divided into several sub controllers conceived separately. Some of these, adopt in turn a hierarchical prioritized structure.
{"title":"Fuzzy pro-active agents as key issue to increase traffic safety for next generation tunnels","authors":"V. Galdi, V. Loia, A. Piccolo, Mario Veniero","doi":"10.1109/FUZZY.2007.4295505","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295505","url":null,"abstract":"Nowadays, in Europe tunnel safety is a problem of great interest. Recent tragic events have led community and national politicians to start a process of legislation harmonization planning investments toward new technologies designed to improve tunnel safety. The purpose of this paper is to present an innovative system aiming at monitoring and steering vehicular flows nearby tunnels with high accidents risk rate. Based on the integration of IC, video, agent and soft computing technologies, the system's mission is to reduce the risk of accidents which occur inside tunnel through the adaptive generation of speed limits and information to the users approximating tunnels. Strict real-time requirements of the applicative context impose our goal being achieved through a parallel hierarchical fuzzy controller (HFC) implemented by means of a fuzzy control agents network (FCAN) allowing the system to gain higher performances. The fuzzy controller is divided into several sub controllers conceived separately. Some of these, adopt in turn a hierarchical prioritized structure.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132556933","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295446
F. Wu, Jie Lu, Guangquan Zhang, D. Ruan
This paper deals with multi-objective decisionmaking problem with fuzzy parameters under a group environment, called fuzzy multi-objective group decisionmaking (FMOGDM). It first presents an FMOGDM method, which integrates fuzzy multi-objective linear programming (FMOLP) with fuzzy group decision making techniques. Based on the method, a fuzzy multiple objective group decision support system (FMOGDSS) is developed. Finally, it gives a case-based example to demonstrate how an FMOLP problem is solved in a group supported by the FMOGDSS.
{"title":"The Development of A Fuzzy Multi-Objective Group Decision Support System","authors":"F. Wu, Jie Lu, Guangquan Zhang, D. Ruan","doi":"10.1109/FUZZY.2007.4295446","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295446","url":null,"abstract":"This paper deals with multi-objective decisionmaking problem with fuzzy parameters under a group environment, called fuzzy multi-objective group decisionmaking (FMOGDM). It first presents an FMOGDM method, which integrates fuzzy multi-objective linear programming (FMOLP) with fuzzy group decision making techniques. Based on the method, a fuzzy multiple objective group decision support system (FMOGDSS) is developed. Finally, it gives a case-based example to demonstrate how an FMOLP problem is solved in a group supported by the FMOGDSS.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"438 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132207572","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295672
H. Berenji, Yan Wang, G. Vachtsevanos
Multi-agent prognostic health and usage monitoring (Multi-PHUM) is proposed in the paper for use in fault diagnosis and prognosis. Georgia Tech has developed a Matlab simulation model of monopropellant propulsion for studying fault diagnosis. Here we apply Multi-PHUM to the fault diagnosis of the monopropellant propulsion system. In particular, regulator failure is discussed here.
{"title":"Case Based Reasoning for Monopropellant Propulsion","authors":"H. Berenji, Yan Wang, G. Vachtsevanos","doi":"10.1109/FUZZY.2007.4295672","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295672","url":null,"abstract":"Multi-agent prognostic health and usage monitoring (Multi-PHUM) is proposed in the paper for use in fault diagnosis and prognosis. Georgia Tech has developed a Matlab simulation model of monopropellant propulsion for studying fault diagnosis. Here we apply Multi-PHUM to the fault diagnosis of the monopropellant propulsion system. In particular, regulator failure is discussed here.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132498952","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295595
R. Alcalá, J. Alcalá-Fdez, M. J. Gacto, F. Herrera
Data mining is most commonly used in attempts to induce association rules from transaction data. Most previous studies focused on binary-valued transaction data. Transaction data in real-world applications, however, usually consists of quantitative values. In the last years, the fuzzy set theory has been applied to data mining for finding interesting association rules in quantitative transactions. Recently, a new rule representation model was presented to perform a genetic lateral tuning of membership functions. It is based on the 2-tuples linguistic representation model allowing us to adjust the context associated to the linguistic label membership functions. Based on the 2-tuples linguistic representation model, we present a new fuzzy data-mining algorithm for extracting both association rules and membership functions by means of an evolutionary learning of the membership functions, using a basic method for mining fuzzy association rules.
{"title":"Genetic Learning of Membership Functions for Mining Fuzzy Association Rules","authors":"R. Alcalá, J. Alcalá-Fdez, M. J. Gacto, F. Herrera","doi":"10.1109/FUZZY.2007.4295595","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295595","url":null,"abstract":"Data mining is most commonly used in attempts to induce association rules from transaction data. Most previous studies focused on binary-valued transaction data. Transaction data in real-world applications, however, usually consists of quantitative values. In the last years, the fuzzy set theory has been applied to data mining for finding interesting association rules in quantitative transactions. Recently, a new rule representation model was presented to perform a genetic lateral tuning of membership functions. It is based on the 2-tuples linguistic representation model allowing us to adjust the context associated to the linguistic label membership functions. Based on the 2-tuples linguistic representation model, we present a new fuzzy data-mining algorithm for extracting both association rules and membership functions by means of an evolutionary learning of the membership functions, using a basic method for mining fuzzy association rules.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117037013","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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