Pub Date : 2009-10-02DOI: 10.1109/FUZZY.2009.5277412
C. J. Carmona, P. González, M. J. Jesús, F. Herrera
The interpretability of the results obtained and the quality measures used both to extract and evaluate the rules are two key aspects of Subgroup Discovery. In this study, we analyse the influence of the type of rule used to extract knowledge in Subgroup Discovery, and the quality measures more adapted to the evolutionary algorithms for Subgroup Discovery developed so far. The adaptation of the NMEF-SD algorithm to extract disjunctive formal norm rules is also presented.
{"title":"An analysis of evolutionary algorithms with different types of fuzzy rules in subgroup discovery","authors":"C. J. Carmona, P. González, M. J. Jesús, F. Herrera","doi":"10.1109/FUZZY.2009.5277412","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277412","url":null,"abstract":"The interpretability of the results obtained and the quality measures used both to extract and evaluate the rules are two key aspects of Subgroup Discovery. In this study, we analyse the influence of the type of rule used to extract knowledge in Subgroup Discovery, and the quality measures more adapted to the evolutionary algorithms for Subgroup Discovery developed so far. The adaptation of the NMEF-SD algorithm to extract disjunctive formal norm rules is also presented.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114880190","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 : 2009-10-02DOI: 10.1109/FUZZY.2009.5277198
Masaki Ando, M. Hagiwara
In this paper, we propose a 3D character creation system using an extraction method of kansei (sensibility) rule with fitness value. In the proposed system, a 3D character reflected kansei of the user is expressed with some constitution attributes. The attributes that are necessary to reflect kansei of the user are extracted as if-then rules by kansei rule extraction method. The consequent part of kansei rule has the fitness. By introducing the fitness, extracted kansei rules have priority. Therefore, kansei rules can be used effectively. 3D characters created by the proposed system and the user's evaluation values are stored as data, and kansei rules are extracted by analyzing the data. The extracted kansei rules are applied to create 3D characters. We have confirmed that the proposed system can create 3D characters reflected kansei of the user through experiments.
{"title":"3D character creation system using Kansei rule with the fitness extraction method","authors":"Masaki Ando, M. Hagiwara","doi":"10.1109/FUZZY.2009.5277198","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277198","url":null,"abstract":"In this paper, we propose a 3D character creation system using an extraction method of kansei (sensibility) rule with fitness value. In the proposed system, a 3D character reflected kansei of the user is expressed with some constitution attributes. The attributes that are necessary to reflect kansei of the user are extracted as if-then rules by kansei rule extraction method. The consequent part of kansei rule has the fitness. By introducing the fitness, extracted kansei rules have priority. Therefore, kansei rules can be used effectively. 3D characters created by the proposed system and the user's evaluation values are stored as data, and kansei rules are extracted by analyzing the data. The extracted kansei rules are applied to create 3D characters. We have confirmed that the proposed system can create 3D characters reflected kansei of the user through experiments.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124146733","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 : 2009-10-02DOI: 10.1109/FUZZY.2009.5277403
Young-Keun Bang, Chil-Heui Lee
In this paper, a multiple prediction system using T-S fuzzy model is presented for time series forecasting. To design predictors with better performance especially for chaos or nonlinear time series, difference data were used as their input, because they reveal the statistical patterns and the regularities concealed in time series more effectively than the original data can. The proposed method consists of three major procedures. First, multiple model fuzzy predictors (MMFPs) are constructed based on the optimal difference candidates. Next, an adaptive drive mechanism (ADM) based on rough sets is designed for the selection of the best one among the multiple predictors according to each input data. Finally, an error compensation mechanism (ECM) based on the cross-correlation analysis is suggested in order to enhance further the prediction performances. Also we show the effectiveness of the proposed method by computer simulation for the various typical time series.
{"title":"Design of adaptive prediction system based on rough sets","authors":"Young-Keun Bang, Chil-Heui Lee","doi":"10.1109/FUZZY.2009.5277403","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277403","url":null,"abstract":"In this paper, a multiple prediction system using T-S fuzzy model is presented for time series forecasting. To design predictors with better performance especially for chaos or nonlinear time series, difference data were used as their input, because they reveal the statistical patterns and the regularities concealed in time series more effectively than the original data can. The proposed method consists of three major procedures. First, multiple model fuzzy predictors (MMFPs) are constructed based on the optimal difference candidates. Next, an adaptive drive mechanism (ADM) based on rough sets is designed for the selection of the best one among the multiple predictors according to each input data. Finally, an error compensation mechanism (ECM) based on the cross-correlation analysis is suggested in order to enhance further the prediction performances. Also we show the effectiveness of the proposed method by computer simulation for the various typical time series.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122261415","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 : 2009-10-02DOI: 10.1109/FUZZY.2009.5277160
A. A. Ramli, J. Watada, W. Pedrycz
Fuzzy regression is one of important methods for data analysis. Fuzzy regression extends the concept of classical regression which has been constructed in the statistical framework. We show that a convex hull method can provide a powerful tool to reduce the computing time, especially for real-time data analysis. The main objective of this study is to propose an efficient real-time fuzzy regression analysis based on the use of convex hull, specifically a Beneath-Beyond algorithm. The reconstruction of convex hull edges depends on incoming vertices while a recomputing procedure can be implemented in real-time. An air pollution data is analyzed by applying the proposed approach. An important role of convex hull is emphasized in particular when dealing with the limitations of linear programming.
{"title":"New perspectives and applications of real-time fuzzy regression","authors":"A. A. Ramli, J. Watada, W. Pedrycz","doi":"10.1109/FUZZY.2009.5277160","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277160","url":null,"abstract":"Fuzzy regression is one of important methods for data analysis. Fuzzy regression extends the concept of classical regression which has been constructed in the statistical framework. We show that a convex hull method can provide a powerful tool to reduce the computing time, especially for real-time data analysis. The main objective of this study is to propose an efficient real-time fuzzy regression analysis based on the use of convex hull, specifically a Beneath-Beyond algorithm. The reconstruction of convex hull edges depends on incoming vertices while a recomputing procedure can be implemented in real-time. An air pollution data is analyzed by applying the proposed approach. An important role of convex hull is emphasized in particular when dealing with the limitations of linear programming.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115803597","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 : 2009-10-02DOI: 10.1109/FUZZY.2009.5277391
Chih-Chin Lai, Cheng-Chih Tsai, Shing‐Tai Pan
In this paper, we introduce an image watermarking scheme using singular value decomposition (SVD) and improved micro-genetic algorithm (micro-GA). In an SVD-based watermarking scheme, the singular values of a cover image are modified by multiple scaling factors to embed the watermark image. Determining proper values of scaling factors to reduce visual artifacts is viewed as an optimization problem and we use the improved micro-GA to search the feasible solution. Experimental results are provided to illustrate the proposed approach is robust to common signal processing attacks.
{"title":"An SVD-based watermarking scheme using improved micro-genetic algorithm","authors":"Chih-Chin Lai, Cheng-Chih Tsai, Shing‐Tai Pan","doi":"10.1109/FUZZY.2009.5277391","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277391","url":null,"abstract":"In this paper, we introduce an image watermarking scheme using singular value decomposition (SVD) and improved micro-genetic algorithm (micro-GA). In an SVD-based watermarking scheme, the singular values of a cover image are modified by multiple scaling factors to embed the watermark image. Determining proper values of scaling factors to reduce visual artifacts is viewed as an optimization problem and we use the improved micro-GA to search the feasible solution. Experimental results are provided to illustrate the proposed approach is robust to common signal processing attacks.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132381043","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 : 2009-10-02DOI: 10.1109/FUZZY.2009.5277176
Y. Kanzawa, Y. Endo, S. Miyamoto
This paper presents a new clustering algorithm, which is based on entropy regularized fuzzy c-lines, can treat data with some errors. First, the tolerance is formulated and introduce into optimization problem of clustering. Next, the problem is solved using Karush-Kuhn-Tucker conditions. Last, the algorithm is constructed based on the results of solving the problem. Some numerical examples for the proposed method are shown.
{"title":"Entropy regularized fuzzy C-lines for data with tolerance","authors":"Y. Kanzawa, Y. Endo, S. Miyamoto","doi":"10.1109/FUZZY.2009.5277176","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277176","url":null,"abstract":"This paper presents a new clustering algorithm, which is based on entropy regularized fuzzy c-lines, can treat data with some errors. First, the tolerance is formulated and introduce into optimization problem of clustering. Next, the problem is solved using Karush-Kuhn-Tucker conditions. Last, the algorithm is constructed based on the results of solving the problem. Some numerical examples for the proposed method are shown.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132401606","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 : 2009-10-02DOI: 10.1109/FUZZY.2009.5277140
S. Yaakob, J. Watada
In workers' evaluation and placement, numerous workers with different skills and expertise may share the same role in an organization, making it hard to select appropriate workers based merely on the assignment relation between role and a job. To bridge the gap between abstract roles and real workers, this paper proposed a workers' placement model capable of evaluating workers' suitability for a specified task according their performance, social and mental factor. For this type of problems, an analysis using a fuzzy number approach promises to be potentially effective. In order to make a more convincing and accurate decision, the relationship among workers is included in the workers' assignment in an industrial environment. Finally candidates are ranked based on their suitability grades to support decision makers in selecting appropriate workers to perform the job. Numerical examples are also presented to demonstrate that the workers' relationship is an important factor and our method is effective for the decision making process.
{"title":"Fuzzy approach for assignment problem","authors":"S. Yaakob, J. Watada","doi":"10.1109/FUZZY.2009.5277140","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277140","url":null,"abstract":"In workers' evaluation and placement, numerous workers with different skills and expertise may share the same role in an organization, making it hard to select appropriate workers based merely on the assignment relation between role and a job. To bridge the gap between abstract roles and real workers, this paper proposed a workers' placement model capable of evaluating workers' suitability for a specified task according their performance, social and mental factor. For this type of problems, an analysis using a fuzzy number approach promises to be potentially effective. In order to make a more convincing and accurate decision, the relationship among workers is included in the workers' assignment in an industrial environment. Finally candidates are ranked based on their suitability grades to support decision makers in selecting appropriate workers to perform the job. Numerical examples are also presented to demonstrate that the workers' relationship is an important factor and our method is effective for the decision making process.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129998072","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 : 2009-10-02DOI: 10.1109/FUZZY.2009.5277365
Keon-Jun Park, Sung-Kwun Oh, W. Pedrycz
In this paper, we introduce the design methodology of interval type-2 fuzzy neural networks (IT2FNN). And to optimize the network we use a real-coded genetic algorithm. IT2FNN is the network of combination between the fuzzy neural network (FNN) and interval type-2 fuzzy set with uncertainty. The antecedent part of the network is composed of the fuzzy division of input space and the consequence part of the network is represented by polynomial functions. The parameters such as the apexes of membership function, uncertainty parameter, the learning rate and the momentum coefficient are optimized using genetic algorithm (GA). The proposed network is evaluated with the performance between the approximation and the generalization abilities.
{"title":"Design of interval type-2 fuzzy neural networks and their optimization using real-coded genetic algorithms","authors":"Keon-Jun Park, Sung-Kwun Oh, W. Pedrycz","doi":"10.1109/FUZZY.2009.5277365","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277365","url":null,"abstract":"In this paper, we introduce the design methodology of interval type-2 fuzzy neural networks (IT2FNN). And to optimize the network we use a real-coded genetic algorithm. IT2FNN is the network of combination between the fuzzy neural network (FNN) and interval type-2 fuzzy set with uncertainty. The antecedent part of the network is composed of the fuzzy division of input space and the consequence part of the network is represented by polynomial functions. The parameters such as the apexes of membership function, uncertainty parameter, the learning rate and the momentum coefficient are optimized using genetic algorithm (GA). The proposed network is evaluated with the performance between the approximation and the generalization abilities.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134036779","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 : 2009-10-02DOI: 10.1109/FUZZY.2009.5277304
P. Villar, Alberto Fernández, F. Herrera
In this contribution we analyse the significance of the granularity level (number of labels) in Fuzzy Rule-Based Classification Systems in the scenario of data-sets with a high imbalance degree. We refer to imbalanced data-sets when the class distribution is not uniform, a situation that it is present in many real application areas. The aim of this work is to adapt the number of fuzzy labels for each problem, applying a fine granularity in those variables which have a higher dispersion of values and a thick granularity in the variables where an excessive number of labels may result irrelevant. We compare this methodology with the use of a fixed number of labels and with the C4.5 decision tree.
{"title":"A genetic learning of the fuzzy rule-based classification system granularity for highly imbalanced data-sets","authors":"P. Villar, Alberto Fernández, F. Herrera","doi":"10.1109/FUZZY.2009.5277304","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277304","url":null,"abstract":"In this contribution we analyse the significance of the granularity level (number of labels) in Fuzzy Rule-Based Classification Systems in the scenario of data-sets with a high imbalance degree. We refer to imbalanced data-sets when the class distribution is not uniform, a situation that it is present in many real application areas. The aim of this work is to adapt the number of fuzzy labels for each problem, applying a fine granularity in those variables which have a higher dispersion of values and a thick granularity in the variables where an excessive number of labels may result irrelevant. We compare this methodology with the use of a fixed number of labels and with the C4.5 decision tree.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132163590","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 : 2009-10-02DOI: 10.1109/FUZZY.2009.5277267
Han Wang, H. Malki, G. Song
This paper presents four types of semi-active control on Magnetorheological (MR) Damper in an experimental base isolation structure model with three degree-of-freedom. The semi-active control methods include proportional-derivative (PD) control, and three fuzzy control methods: rule-based fuzzy logic control, auto-tuning fuzzy PD control, and discrete fuzzy PD control. The main purpose is to compare the response effect between passive control methods and semi-active control methods, and also compare within semi-active controls. The results of both passive controls and semi-active controls in experiments are presented. From the results, semi-active controls are shown more adaptive than passive control for this model when the earthquake type is unknown. Moreover, auto-tuning fuzzy PD control is proved to have relatively best performance among all control methods.
{"title":"Fuzzy semi-active control of MR damper for structural base isolation","authors":"Han Wang, H. Malki, G. Song","doi":"10.1109/FUZZY.2009.5277267","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277267","url":null,"abstract":"This paper presents four types of semi-active control on Magnetorheological (MR) Damper in an experimental base isolation structure model with three degree-of-freedom. The semi-active control methods include proportional-derivative (PD) control, and three fuzzy control methods: rule-based fuzzy logic control, auto-tuning fuzzy PD control, and discrete fuzzy PD control. The main purpose is to compare the response effect between passive control methods and semi-active control methods, and also compare within semi-active controls. The results of both passive controls and semi-active controls in experiments are presented. From the results, semi-active controls are shown more adaptive than passive control for this model when the earthquake type is unknown. Moreover, auto-tuning fuzzy PD control is proved to have relatively best performance among all control methods.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114476050","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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