Pub Date : 2003-05-25DOI: 10.1109/FUZZ.2003.1209441
J. Sousa, S. Madeira, U. Kaymak
This paper presents the results of one application of target selection in direct marketing: the mailing campaigns of a charity organization, where the clients are selected based on the expected amount of donation they are going to make. Target selection is an important data mining problem for which several modeling techniques have been used. Statistical regression, neural networks, decision trees, and clustering are the most utilized techniques. Fuzzy clustering can also be applied to target selection. In this paper, traditional and fuzzy techniques are compared by using cross-validation measures. The four techniques are applied based on recency, frequency and monetary value measures. The application to mailing campaigns of a charity organization, showed that fuzzy modeling obtains results similar to those of other classical target selection techniques.
{"title":"Modeling charity donations using target selection for revenue maximization","authors":"J. Sousa, S. Madeira, U. Kaymak","doi":"10.1109/FUZZ.2003.1209441","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209441","url":null,"abstract":"This paper presents the results of one application of target selection in direct marketing: the mailing campaigns of a charity organization, where the clients are selected based on the expected amount of donation they are going to make. Target selection is an important data mining problem for which several modeling techniques have been used. Statistical regression, neural networks, decision trees, and clustering are the most utilized techniques. Fuzzy clustering can also be applied to target selection. In this paper, traditional and fuzzy techniques are compared by using cross-validation measures. The four techniques are applied based on recency, frequency and monetary value measures. The application to mailing campaigns of a charity organization, showed that fuzzy modeling obtains results similar to those of other classical target selection techniques.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133034356","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 : 2003-05-25DOI: 10.1109/FUZZ.2003.1206618
G. Ritter, L. Iancu, G. Urcid
Recent advances in neurobiology and the biophysics of neural computation have brought to the foreground the importance of dendritic structures of neurons. These structures are now viewed as the primary basic computational units of the neuron, capable of realizing logical operations. Based on these new biophysical neural models, we develop a new paradigm for single layer perceptrons that incorporates dendritic processes. The basic computational processes in dendrites as well as neurons are based on lattice algebra. The computational capabilities of this new perceptron model is demonstrated by means of several illustrative examples and two theorems.
{"title":"Morphological perceptrons with dendritic structure","authors":"G. Ritter, L. Iancu, G. Urcid","doi":"10.1109/FUZZ.2003.1206618","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1206618","url":null,"abstract":"Recent advances in neurobiology and the biophysics of neural computation have brought to the foreground the importance of dendritic structures of neurons. These structures are now viewed as the primary basic computational units of the neuron, capable of realizing logical operations. Based on these new biophysical neural models, we develop a new paradigm for single layer perceptrons that incorporates dendritic processes. The basic computational processes in dendrites as well as neurons are based on lattice algebra. The computational capabilities of this new perceptron model is demonstrated by means of several illustrative examples and two theorems.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114248330","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 : 2003-05-25DOI: 10.1109/FUZZ.2003.1209454
Jiwen Chen, Jianhua Chen, G. Kemp
In this paper, a hybrid decision tree learning approach is presented that combines fuzzy C-means method and the ID3 algorithm in decision tree construction from continuous-valued features. The fuzzy C-means method is applied to find a number of central means for each continuous-valued feature and thus discretize such features. The ID3 algorithm is subsequently used to build a decision tree from the discretized data. Preliminary experiments using a real-world time-series data set from the Louisiana coast are reported that compare our method with the OC1 system for oblique decision tree learning. The experiment results seem to suggest that the proposed hybrid method achieves better or comparable classification accuracy.
{"title":"Fuzzy clustering and decision tree learning for time-series tidal data classification","authors":"Jiwen Chen, Jianhua Chen, G. Kemp","doi":"10.1109/FUZZ.2003.1209454","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209454","url":null,"abstract":"In this paper, a hybrid decision tree learning approach is presented that combines fuzzy C-means method and the ID3 algorithm in decision tree construction from continuous-valued features. The fuzzy C-means method is applied to find a number of central means for each continuous-valued feature and thus discretize such features. The ID3 algorithm is subsequently used to build a decision tree from the discretized data. Preliminary experiments using a real-world time-series data set from the Louisiana coast are reported that compare our method with the OC1 system for oblique decision tree learning. The experiment results seem to suggest that the proposed hybrid method achieves better or comparable classification accuracy.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117300471","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 : 2003-05-25DOI: 10.1109/FUZZ.2003.1209358
C. Aouiti, A. Alimi, F. Karray, A. Maalej
We propose an evolutionary method for the design of Beta fuzzy systems (BFS). Classical training algorithms start with a predetermined number of fuzzy rules for fuzzy systems. Generally speaking, the fuzzy system created is either insufficient or over-complicated. This paper describes a hierarchical genetic learning model of the BFS. In order to examine the performance of the proposed algorithm, it is used for the identification of an induction machine fuzzy plant model. The results obtained have been encouraging.
{"title":"Evolutionary approach for the beta function based fuzzy systems","authors":"C. Aouiti, A. Alimi, F. Karray, A. Maalej","doi":"10.1109/FUZZ.2003.1209358","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209358","url":null,"abstract":"We propose an evolutionary method for the design of Beta fuzzy systems (BFS). Classical training algorithms start with a predetermined number of fuzzy rules for fuzzy systems. Generally speaking, the fuzzy system created is either insufficient or over-complicated. This paper describes a hierarchical genetic learning model of the BFS. In order to examine the performance of the proposed algorithm, it is used for the identification of an induction machine fuzzy plant model. The results obtained have been encouraging.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117302063","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 : 2003-05-25DOI: 10.1109/FUZZ.2003.1206564
Alan Wee-Chung Liew, Hong Yan
A fuzzy c-means based adaptive clustering algorithm is proposed for the fuzzy segmentation of 3D MR brain images, which are typically corrupted by noise and intensity non-uniformity (INU) artifact. The proposed algorithm enforces the spatial continuity constraint to account for the spatial correlations between image voxels, resulting in the suppression of noise and classification ambiguity. The INU artifact is compensated for by the introduction of a pseudo-3D bias field, which is modeled as a stack of smooth B-spline surfaces with continuity enforced across slices. The efficacy of the proposed algorithm is demonstrated experimentally using both simulated and real MR images.
{"title":"Adaptive fuzzy segmentation of 3D MR brain images","authors":"Alan Wee-Chung Liew, Hong Yan","doi":"10.1109/FUZZ.2003.1206564","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1206564","url":null,"abstract":"A fuzzy c-means based adaptive clustering algorithm is proposed for the fuzzy segmentation of 3D MR brain images, which are typically corrupted by noise and intensity non-uniformity (INU) artifact. The proposed algorithm enforces the spatial continuity constraint to account for the spatial correlations between image voxels, resulting in the suppression of noise and classification ambiguity. The INU artifact is compensated for by the introduction of a pseudo-3D bias field, which is modeled as a stack of smooth B-spline surfaces with continuity enforced across slices. The efficacy of the proposed algorithm is demonstrated experimentally using both simulated and real MR images.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"445 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123590320","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 : 2003-05-25DOI: 10.1109/FUZZ.2003.1209370
N. Kubota, M. Mihara
This paper deals with multi-objective behavior coordination of multiple robots interacting with a quasi-ecosystem which is composed of insects and plants. In this ecosystem, there co-exist plants and insects according to specific reproduction rules. In general, the inhabiting area of each species is localized owing to geographical, climatic, and ecological factors. This indicates the population density of each species in one area is different from another according to local environmental conditions. In this study. multiple robots are introduced in order to maintain the ecosystem. Each robot takes actions based on multi-objective behavior coordination integrating several action outputs. However, the robot must select its suitable area in order to adapt to the current state of the quasi-ecosystem that might change dynamically. In this paper, we discuss target selection for insect removing and plant reaping behaviors through several computer simulations in a dynamically changing environment.
{"title":"Multi-objective behavior coordination of multiple robots interacting with a dynamic environment","authors":"N. Kubota, M. Mihara","doi":"10.1109/FUZZ.2003.1209370","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209370","url":null,"abstract":"This paper deals with multi-objective behavior coordination of multiple robots interacting with a quasi-ecosystem which is composed of insects and plants. In this ecosystem, there co-exist plants and insects according to specific reproduction rules. In general, the inhabiting area of each species is localized owing to geographical, climatic, and ecological factors. This indicates the population density of each species in one area is different from another according to local environmental conditions. In this study. multiple robots are introduced in order to maintain the ecosystem. Each robot takes actions based on multi-objective behavior coordination integrating several action outputs. However, the robot must select its suitable area in order to adapt to the current state of the quasi-ecosystem that might change dynamically. In this paper, we discuss target selection for insect removing and plant reaping behaviors through several computer simulations in a dynamically changing environment.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122665506","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 : 2003-05-25DOI: 10.1109/FUZZ.2003.1209403
Chiang-Cheng Chiang, Wen-Hao Wang
Based on the combination of the H/sup /spl infin// optimal control with fuzzy logic control and the simple adaptation laws, this paper presents a new and feasible design algorithm to synthesize a decentralized robust adaptive fuzzy controller which can easily tackle the output tracking control problem of large-scale nonlinear uncertain systems without the knowledge of the upper bounds on the norm of the uncertainties.
{"title":"Decentralized robust adaptive fuzzy controller for large-scale nonlinear uncertain systems","authors":"Chiang-Cheng Chiang, Wen-Hao Wang","doi":"10.1109/FUZZ.2003.1209403","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209403","url":null,"abstract":"Based on the combination of the H/sup /spl infin// optimal control with fuzzy logic control and the simple adaptation laws, this paper presents a new and feasible design algorithm to synthesize a decentralized robust adaptive fuzzy controller which can easily tackle the output tracking control problem of large-scale nonlinear uncertain systems without the knowledge of the upper bounds on the norm of the uncertainties.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122713418","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 : 2003-05-25DOI: 10.1109/FUZZ.2003.1209455
Pei-Yi Hao, J. Chiang
Fuzziness must he considered in systems where human estimation is influential. A model of such a vague phenomenon might he represented as a fuzzy system equation which can he described by the fuzzy functions defined by Zadeh’s extension principle. In this paper, we incorporate the concept of fuzzy set theory into the support vector machine (SVM) regression. The parameters to he identified in SVM regression, such as the components within the weight vector and the bias term, are fuzzy numbers, and the desired outputs in training samples are also fuzzy numbers. This integration preserves the benefits of SVM regression model and fuzzy regression model, where the SVM learning theory characterizes properties of learning machines which enable them to generalize well the unseen data and the fuzzy set theory might he very useful for finding a fuzzy structure in an evaluation system.
{"title":"A fuzzy model of support vector machine regression","authors":"Pei-Yi Hao, J. Chiang","doi":"10.1109/FUZZ.2003.1209455","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209455","url":null,"abstract":"Fuzziness must he considered in systems where human estimation is influential. A model of such a vague phenomenon might he represented as a fuzzy system equation which can he described by the fuzzy functions defined by Zadeh’s extension principle. In this paper, we incorporate the concept of fuzzy set theory into the support vector machine (SVM) regression. The parameters to he identified in SVM regression, such as the components within the weight vector and the bias term, are fuzzy numbers, and the desired outputs in training samples are also fuzzy numbers. This integration preserves the benefits of SVM regression model and fuzzy regression model, where the SVM learning theory characterizes properties of learning machines which enable them to generalize well the unseen data and the fuzzy set theory might he very useful for finding a fuzzy structure in an evaluation system.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122859113","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 : 2003-05-25DOI: 10.1109/FUZZ.2003.1209391
M. F. Zarandi, S. Saghiri
Supplier selection is understood as one of the key processes in strategic decision making level in Supply Chains (SC). This paper develops a comprehensive multiple products and multiple suppliers model for this process. Moreover, various targets are discussed and analyzed in the form of objectives, in addition to related constraints. Such model development is fulfilled in a real-world situation with wide ranges of uncertainties. In this paper, a fuzzy decision making model is presented. In the proposed Fuzzy Multiple Objectives Decision Making (FMODM) model, all goals, constraints, variables and coefficients are fuzzy. It is shown that with the application of the fuzzy methodology, the complex multi-objective problem is converted to a single one that can be solved and interpreted easily.
{"title":"A comprehensive fuzzy multi-objective model for supplier selection process","authors":"M. F. Zarandi, S. Saghiri","doi":"10.1109/FUZZ.2003.1209391","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209391","url":null,"abstract":"Supplier selection is understood as one of the key processes in strategic decision making level in Supply Chains (SC). This paper develops a comprehensive multiple products and multiple suppliers model for this process. Moreover, various targets are discussed and analyzed in the form of objectives, in addition to related constraints. Such model development is fulfilled in a real-world situation with wide ranges of uncertainties. In this paper, a fuzzy decision making model is presented. In the proposed Fuzzy Multiple Objectives Decision Making (FMODM) model, all goals, constraints, variables and coefficients are fuzzy. It is shown that with the application of the fuzzy methodology, the complex multi-objective problem is converted to a single one that can be solved and interpreted easily.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123837859","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 : 2003-05-25DOI: 10.1109/FUZZ.2003.1209379
J. Economou, A. Tsourdos, P. Luk, B. White
In this paper an effective conventional/intelligent approach has been described which solves the problem of actuator competing factors for the class of indirect all-wheel drive skid-steer mobile robots. The above arrangement allows all the wheels to be independently driven in order to meet the different variations in the tyre-ground interface. However this wheel independence in practice can result in the independent wheel controllers to compete in order to achieve their individual design objective. It has been observed from real mobile robots that this phenomenon results in higher than usual current requests due to the force mismatch between the different wheel actuators which strain the energy system faster than usual and consequently result in a higher risk of being unsuccessful when operating autonomously in demanding environments such as a planetary rover, a construction or a mining robot.
{"title":"Intelligent control of a multi-actuator mobile robot with competing factors","authors":"J. Economou, A. Tsourdos, P. Luk, B. White","doi":"10.1109/FUZZ.2003.1209379","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209379","url":null,"abstract":"In this paper an effective conventional/intelligent approach has been described which solves the problem of actuator competing factors for the class of indirect all-wheel drive skid-steer mobile robots. The above arrangement allows all the wheels to be independently driven in order to meet the different variations in the tyre-ground interface. However this wheel independence in practice can result in the independent wheel controllers to compete in order to achieve their individual design objective. It has been observed from real mobile robots that this phenomenon results in higher than usual current requests due to the force mismatch between the different wheel actuators which strain the energy system faster than usual and consequently result in a higher risk of being unsuccessful when operating autonomously in demanding environments such as a planetary rover, a construction or a mining robot.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125282802","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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