Pub Date : 2007-07-23DOI: 10.1109/FUZZY.2007.4295438
À. Nebot, Jesús Antonio Acosta Sarmiento, V. Mugica
In this work four genetic fuzzy system are applied to an environmental problem, i.e. modeling ozone concentrations in Mexico City metropolitan area. These hybrid systems are composed by the Fuzzy Inductive Reasoning (FIR) methodology and different genetic algorithms (GAs) that takes charge of determining, in an automatic way, the fuzzification parameters. Mexico is the second country in the world with high air pollution levels. The main air pollution problem that has been identified in Mexico City metropolitan area is the formation of photochemical smog, primarily ozone. This toxic gas can produce harmful effects on the population's health. The study and development of modeling methodologies that allow the capturing of ozone behavior becomes an important task when it is intended to predict contingencies before they are produced.
{"title":"Environmental Modeling by means of Genetic Fuzzy Systems","authors":"À. Nebot, Jesús Antonio Acosta Sarmiento, V. Mugica","doi":"10.1109/FUZZY.2007.4295438","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295438","url":null,"abstract":"In this work four genetic fuzzy system are applied to an environmental problem, i.e. modeling ozone concentrations in Mexico City metropolitan area. These hybrid systems are composed by the Fuzzy Inductive Reasoning (FIR) methodology and different genetic algorithms (GAs) that takes charge of determining, in an automatic way, the fuzzification parameters. Mexico is the second country in the world with high air pollution levels. The main air pollution problem that has been identified in Mexico City metropolitan area is the formation of photochemical smog, primarily ozone. This toxic gas can produce harmful effects on the population's health. The study and development of modeling methodologies that allow the capturing of ozone behavior becomes an important task when it is intended to predict contingencies before they are produced.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"9 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":"133057499","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.2007.4295577
Honggang Wang, Hua Fang, H. Sharif, Zhenyuan Wang
In this paper, we propose a new nonlinear classifier based on a generalized Choquet integral with signed fuzzy measures to enhance the classification power by capturing all possible interactions among two or more attributes. A special genetic algorithm is designed to implement this classification optimization with fast convergence. Instead of using a discrete misclassification rate, the objective function to be optimized in this research is a continuous Choquet distance with a penalty coefficient for misclassified points. The numerical experiment shows that the special genetic algorithm effectively solves the nonlinear classification problem and this nonlinear classifier accurately identifies classes.
{"title":"Nonlinear Classification by Genetic Algorithm with Signed Fuzzy Measure","authors":"Honggang Wang, Hua Fang, H. Sharif, Zhenyuan Wang","doi":"10.1109/FUZZY.2007.4295577","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295577","url":null,"abstract":"In this paper, we propose a new nonlinear classifier based on a generalized Choquet integral with signed fuzzy measures to enhance the classification power by capturing all possible interactions among two or more attributes. A special genetic algorithm is designed to implement this classification optimization with fast convergence. Instead of using a discrete misclassification rate, the objective function to be optimized in this research is a continuous Choquet distance with a penalty coefficient for misclassified points. The numerical experiment shows that the special genetic algorithm effectively solves the nonlinear classification problem and this nonlinear classifier accurately identifies classes.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"60 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":"133503463","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.2007.4295352
P. Lingras, Richard Jensen
This paper provides a broad overview of logical and black box approaches to fuzzy and rough hybridization. The logical approaches include theoretical, supervised learning, feature selection, and unsupervised learning. The black box approaches consist of neural and evolutionary computing. Since both theories originated in the expert system domain, there are a number of research proposals that combine rough and fuzzy concepts in supervised learning. However, continuing developments of rough and fuzzy extensions to clustering, neurocomputing, and genetic algorithms make hybrid approaches in these areas a potentially rewarding research opportunity as well.
{"title":"Survey of Rough and Fuzzy Hybridization","authors":"P. Lingras, Richard Jensen","doi":"10.1109/FUZZY.2007.4295352","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295352","url":null,"abstract":"This paper provides a broad overview of logical and black box approaches to fuzzy and rough hybridization. The logical approaches include theoretical, supervised learning, feature selection, and unsupervised learning. The black box approaches consist of neural and evolutionary computing. Since both theories originated in the expert system domain, there are a number of research proposals that combine rough and fuzzy concepts in supervised learning. However, continuing developments of rough and fuzzy extensions to clustering, neurocomputing, and genetic algorithms make hybrid approaches in these areas a potentially rewarding research opportunity as well.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"22 6S 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":"133265622","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.2007.4295518
Neil MacParthaláin, Q. Shen, Richard Jensen
Feature selection (FS) is a technique for dimensionality reduction. Its aims are to select a subset of the original features of a dataset which are rich in the most useful information. The benefits include improved data visualisation, transparency, a reduction in training and utilisation times and potentially, improved prediction performance. Many approaches based on rough set theory have employed the dependency function which is based on the information contained in the lower approximation as an evaluation step in the FS process with much success. This paper presents a novel rough set FS technique which uses the information of both the lower approximation dependency value and a distance metric for the consideration of objects in the boundary region. The use of this measure in rough set feature selection can result in smaller subset sizes than those obtained using the dependency function alone.
{"title":"Distance Measure Assisted Rough Set Feature Selection","authors":"Neil MacParthaláin, Q. Shen, Richard Jensen","doi":"10.1109/FUZZY.2007.4295518","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295518","url":null,"abstract":"Feature selection (FS) is a technique for dimensionality reduction. Its aims are to select a subset of the original features of a dataset which are rich in the most useful information. The benefits include improved data visualisation, transparency, a reduction in training and utilisation times and potentially, improved prediction performance. Many approaches based on rough set theory have employed the dependency function which is based on the information contained in the lower approximation as an evaluation step in the FS process with much success. This paper presents a novel rough set FS technique which uses the information of both the lower approximation dependency value and a distance metric for the consideration of objects in the boundary region. The use of this measure in rough set feature selection can result in smaller subset sizes than those obtained using the dependency function alone.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"25 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":"132176151","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.2007.4295464
F. Hernandes, M. T. Lamata, M. Takahashi, A. Yamakami, J. Verdegay
The problem of finding the maximum flow between a source and a destination node in a network with uncertainties in its capacities is an important problem of network flows, since it has a wide range of applications in different areas (telecommunications, transportations, manufacturing, etc) and therefore deserves special attention. However, due to complexity in working with this kind of problems, there are a few algorithms in literature, which demand that the user informs the desirable maximum flow, which is difficult when the network is the large scale. In this paper, an algorithm based on the classic algorithm of Ford-Fulkerson is proposed. The algorithm uses the technique of the incremental graph and it does not request that the decisionmaker informs the desirable flow, in contrast of the main works of literature. The uncertainties of the parameters are resolved using the fuzzy sets theory.
{"title":"An Algorithm for the Fuzzy Maximum Flow Problem","authors":"F. Hernandes, M. T. Lamata, M. Takahashi, A. Yamakami, J. Verdegay","doi":"10.1109/FUZZY.2007.4295464","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295464","url":null,"abstract":"The problem of finding the maximum flow between a source and a destination node in a network with uncertainties in its capacities is an important problem of network flows, since it has a wide range of applications in different areas (telecommunications, transportations, manufacturing, etc) and therefore deserves special attention. However, due to complexity in working with this kind of problems, there are a few algorithms in literature, which demand that the user informs the desirable maximum flow, which is difficult when the network is the large scale. In this paper, an algorithm based on the classic algorithm of Ford-Fulkerson is proposed. The algorithm uses the technique of the incremental graph and it does not request that the decisionmaker informs the desirable flow, in contrast of the main works of literature. The uncertainties of the parameters are resolved using the fuzzy sets theory.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"113 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":"132717780","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.2007.4295335
Chia-Feng Juang, Hao-Jung Huang, Chun-Ming Lu
An ant colony optimization (ACO) application to a fuzzy controller design, called ACO-FC, is proposed in this paper for improving design efficiency. A fuzzy controller's antecedent part, i.e., the "if" part of its composing fuzzy if-then rules, is partitioned in grid-type, and all candidate rule consequent values are then listed. An ant tour is regarded as a combination of consequent values selected from every rule. A pheromone matrix among all candidate consequent values is constructed. Searching for the best one among all combinations of rule consequent values is based mainly on the pheromone matrix. The proposed ACO-FC performance is shown to be better than other evolutionary design methods on one simulation example.
{"title":"Fuzzy Controller Design by Ant Colony Optimization","authors":"Chia-Feng Juang, Hao-Jung Huang, Chun-Ming Lu","doi":"10.1109/FUZZY.2007.4295335","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295335","url":null,"abstract":"An ant colony optimization (ACO) application to a fuzzy controller design, called ACO-FC, is proposed in this paper for improving design efficiency. A fuzzy controller's antecedent part, i.e., the \"if\" part of its composing fuzzy if-then rules, is partitioned in grid-type, and all candidate rule consequent values are then listed. An ant tour is regarded as a combination of consequent values selected from every rule. A pheromone matrix among all candidate consequent values is constructed. Searching for the best one among all combinations of rule consequent values is based mainly on the pheromone matrix. The proposed ACO-FC performance is shown to be better than other evolutionary design methods on one simulation example.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"97 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":"115116162","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.2007.4295523
G. Cerofolini, P. Amato
The logical structure of a formal theory of general chemistry, where the properties of all molecules are deduced from those of the constituting atoms and bonds (whose properties are assigned a priori), has been constructed. This theory, however, admits the material world as a model ("the theory represents the reality") only if its mathematical structure is based on fuzzy arithmetics. In this way fuzzy logic enters as the basic element of foundational theory like chemistry, rather than simply a tool to manage poorly defined situations.
{"title":"Fuzzy Chemistry ߞ An Axiomatic Theory for General Chemistry","authors":"G. Cerofolini, P. Amato","doi":"10.1109/FUZZY.2007.4295523","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295523","url":null,"abstract":"The logical structure of a formal theory of general chemistry, where the properties of all molecules are deduced from those of the constituting atoms and bonds (whose properties are assigned a priori), has been constructed. This theory, however, admits the material world as a model (\"the theory represents the reality\") only if its mathematical structure is based on fuzzy arithmetics. In this way fuzzy logic enters as the basic element of foundational theory like chemistry, rather than simply a tool to manage poorly defined situations.","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":"117002268","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.2007.4295519
G. Schaefer, T. Nakashima, Y. Yokota, H. Ishibuchi
Microarray expression studies measure, through a hybridisation process, the levels of genes expressed in biological samples. Knowledge gained from these studies is deemed increasingly important due to its potential of contributing to the understanding of fundamental questions in biology and clinical medicine. One important aspect of microarray expression analysis is the classification of the recorded samples which poses many challenges due to the vast number of recorded expression levels compared to the relatively small numbers of analysed samples. In this paper we show how fuzzy rule-based classification can be applied successfully to analyse gene expression data. The generated classifier consists of an ensemble of fuzzy if-then rules which together provide a reliable and accurate classification of the underlying data. Experimental results on several standard microarray datasets confirm the efficacy of the approach.
{"title":"Fuzzy Classification of Gene Expression Data","authors":"G. Schaefer, T. Nakashima, Y. Yokota, H. Ishibuchi","doi":"10.1109/FUZZY.2007.4295519","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295519","url":null,"abstract":"Microarray expression studies measure, through a hybridisation process, the levels of genes expressed in biological samples. Knowledge gained from these studies is deemed increasingly important due to its potential of contributing to the understanding of fundamental questions in biology and clinical medicine. One important aspect of microarray expression analysis is the classification of the recorded samples which poses many challenges due to the vast number of recorded expression levels compared to the relatively small numbers of analysed samples. In this paper we show how fuzzy rule-based classification can be applied successfully to analyse gene expression data. The generated classifier consists of an ensemble of fuzzy if-then rules which together provide a reliable and accurate classification of the underlying data. Experimental results on several standard microarray datasets confirm the efficacy of the approach.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"16 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":"117031009","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.2007.4295511
C. Borgelt
Approaches based on conditional independence tests are among the most popular methods for learning graphical models from data. Due to the predominance of Bayesian networks in the field, they are usually developed for directed graphs. For possibilistic networks of a certain kind, however, undirected graphs are a more natural basis and thus algorithms for learning undirected graphs are desirable in this area. In this paper I present an algorithm for learning undirected graphical models, which is derived from the well-known Cheng-Bell-Liu algorithm. Its main advantage is the lower number of conditional independence tests that are needed, while it achieves results of comparable quality.
{"title":"Learning Undirected Possibilistic Networks with Conditional Independence Tests","authors":"C. Borgelt","doi":"10.1109/FUZZY.2007.4295511","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295511","url":null,"abstract":"Approaches based on conditional independence tests are among the most popular methods for learning graphical models from data. Due to the predominance of Bayesian networks in the field, they are usually developed for directed graphs. For possibilistic networks of a certain kind, however, undirected graphs are a more natural basis and thus algorithms for learning undirected graphs are desirable in this area. In this paper I present an algorithm for learning undirected graphical models, which is derived from the well-known Cheng-Bell-Liu algorithm. Its main advantage is the lower number of conditional independence tests that are needed, while it achieves results of comparable quality.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"6 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":"123658072","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.2007.4295443
F. Bobillo, U. Straccia
Fuzzy description logics (fuzzy DLs) have been proposed as a language to describe structured knowledge with vague concepts. It is well known that the choice of the fuzzy operators may determine some logical properties. However, up to date the study of fuzzy DLs has been restricted to the Lukasiewicz logic and the "Zadeh semantics". In this work, we propose a novel semantics combining the common product t-norm with the standard negation. We show some interesting properties of the logic and propose a reasoning algorithm based on a mixture of tableaux rules and the reduction to mixed integer quadratically constrained programming.
{"title":"A Fuzzy Description Logic with Product T-norm","authors":"F. Bobillo, U. Straccia","doi":"10.1109/FUZZY.2007.4295443","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295443","url":null,"abstract":"Fuzzy description logics (fuzzy DLs) have been proposed as a language to describe structured knowledge with vague concepts. It is well known that the choice of the fuzzy operators may determine some logical properties. However, up to date the study of fuzzy DLs has been restricted to the Lukasiewicz logic and the \"Zadeh semantics\". In this work, we propose a novel semantics combining the common product t-norm with the standard negation. We show some interesting properties of the logic and propose a reasoning algorithm based on a mixture of tableaux rules and the reduction to mixed integer quadratically constrained programming.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"27 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":"117213064","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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