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.2003.1206568
M. Murakami, N. Honda, J. Nishino
This paper presents a hardware system that implements the active learning method (ALM), a methodology of soft computing. ALM has processing engines called IDS, which are tasked with extracting useful information from a system subject to modeling. In realizing ALM in hardware, it will be desirable in terms of processing nature, performance, and scalability to utilize dedicated hardware for IDS. This paper primarily describes the actual hardware design of an IDS module, and shows the findings of tests of an ALM hardware system that implemented this module.
{"title":"A hardware design for a new learning system based on fuzzy concepts","authors":"M. Murakami, N. Honda, J. Nishino","doi":"10.1109/FUZZ.2003.1206568","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1206568","url":null,"abstract":"This paper presents a hardware system that implements the active learning method (ALM), a methodology of soft computing. ALM has processing engines called IDS, which are tasked with extracting useful information from a system subject to modeling. In realizing ALM in hardware, it will be desirable in terms of processing nature, performance, and scalability to utilize dedicated hardware for IDS. This paper primarily describes the actual hardware design of an IDS module, and shows the findings of tests of an ALM hardware system that implemented this module.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"98 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":"124357974","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}
Pub Date : 2003-05-25DOI: 10.1109/FUZZ.2003.1206611
H. Kargupta, Kun Liu, Souptik Datta, Jessica Ryan, K. Sivakumar
Defending the safety of an open society from terrorism or other similar threats requires intelligent but careful ways to monitor different types of activities and transactions in the electronic media. Data mining techniques are playing an increasingly important role in sifting through large amount of data in search of useful patterns that might help us in securing our safety. Although the objective of this class of data mining applications is very well justified, they also open up the possibility of misusing personal information by malicious people with access to the sensitive data. This brings up the following question: Can we design data mining techniques that are sensitive to privacy? Several researchers are currently working on a class of data mining algorithms that work without directly accessing the sensitive data in their original form. This paper considers the problem of mining distributed data in a privacy-sensitive manner. It first points out the problems of some of the existing privacy-sensitive data mining techniques that make use of additive random noise to hide sensitive information. Next it briefly reviews some new approaches that make use of random projection matrices for computing statistical aggregates from sensitive data.
{"title":"Homeland security and privacy sensitive data mining from multi-party distributed resources","authors":"H. Kargupta, Kun Liu, Souptik Datta, Jessica Ryan, K. Sivakumar","doi":"10.1109/FUZZ.2003.1206611","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1206611","url":null,"abstract":"Defending the safety of an open society from terrorism or other similar threats requires intelligent but careful ways to monitor different types of activities and transactions in the electronic media. Data mining techniques are playing an increasingly important role in sifting through large amount of data in search of useful patterns that might help us in securing our safety. Although the objective of this class of data mining applications is very well justified, they also open up the possibility of misusing personal information by malicious people with access to the sensitive data. This brings up the following question: Can we design data mining techniques that are sensitive to privacy? Several researchers are currently working on a class of data mining algorithms that work without directly accessing the sensitive data in their original form. This paper considers the problem of mining distributed data in a privacy-sensitive manner. It first points out the problems of some of the existing privacy-sensitive data mining techniques that make use of additive random noise to hide sensitive information. Next it briefly reviews some new approaches that make use of random projection matrices for computing statistical aggregates from sensitive data.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"58 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":"134469610","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.2003.1209399
Zenglian Liu, C. Su, J. Svoboda
Wing rock is a highly nonlinear phenomenon in which the aircraft undergoes limit-cycle roll oscillations at high angles of attack (AOA). In this paper, a simple fuzzy PD control method is employed for wing-rock suppression and tracking because fuzzy PD controller has the same performance as the conventional PD controller for linear processes, yet improves the control capability for nonlinear and uncertain processes. Simulations at various initial conditions and different AOAs demonstrate the effectiveness and robustness of the proposed scheme. Comparison with other fuzzy PD controllers in literatures is also conducted. It shows that the proposed fuzzy controller can control wing-rock with complete and fast control effect in a wide range of AOA.
{"title":"Control of wing rock using fuzzy PD controller","authors":"Zenglian Liu, C. Su, J. Svoboda","doi":"10.1109/FUZZ.2003.1209399","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209399","url":null,"abstract":"Wing rock is a highly nonlinear phenomenon in which the aircraft undergoes limit-cycle roll oscillations at high angles of attack (AOA). In this paper, a simple fuzzy PD control method is employed for wing-rock suppression and tracking because fuzzy PD controller has the same performance as the conventional PD controller for linear processes, yet improves the control capability for nonlinear and uncertain processes. Simulations at various initial conditions and different AOAs demonstrate the effectiveness and robustness of the proposed scheme. Comparison with other fuzzy PD controllers in literatures is also conducted. It shows that the proposed fuzzy controller can control wing-rock with complete and fast control effect in a wide range of AOA.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"4 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":"134553820","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.2003.1206600
B. L. Saux, Nizar Grira, N. Boujemaa
To allow efficient browsing of large image collection, we have to provide a summary of its visual content. We present in this paper a new robust approach to categorize image databases: Adaptive Robust Competition with Proximity-Based Merging (ARC-M). This algorithm relies on a non-supervised database categorization, coupled with a selection of prototypes in each resulting category. Each image is represented by a high-dimensional vector in the feature space. A principal component analysis is performed for every feature to reduce dimensionality. Then, clustering is performed in challenging conditions by minimizing a Competitive Agglomeration objective function with an extra noise cluster to collect outliers. Agglomeration is improved by a merging process based on cluster proximity verification.
{"title":"Adaptive robust clustering with proximity-based merging for video-summary","authors":"B. L. Saux, Nizar Grira, N. Boujemaa","doi":"10.1109/FUZZ.2003.1206600","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1206600","url":null,"abstract":"To allow efficient browsing of large image collection, we have to provide a summary of its visual content. We present in this paper a new robust approach to categorize image databases: Adaptive Robust Competition with Proximity-Based Merging (ARC-M). This algorithm relies on a non-supervised database categorization, coupled with a selection of prototypes in each resulting category. Each image is represented by a high-dimensional vector in the feature space. A principal component analysis is performed for every feature to reduce dimensionality. Then, clustering is performed in challenging conditions by minimizing a Competitive Agglomeration objective function with an extra noise cluster to collect outliers. Agglomeration is improved by a merging process based on cluster proximity verification.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"25 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":"132009170","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.2003.1209437
J. Baldwin, E. D. Tomaso
This paper deals with the development of a theory on bayesian networks. It proposes a modified algorithm for solving knowledge querying and information updating, when dealing with continuous variables and with probabilistic and uncertain instantiations. Fuzzy sets are used to rewrite the information contained in a database in order to reduce the complexity of the automatic learning of a bayesian net from data.
{"title":"Inference and learning in fuzzy bayesian networks","authors":"J. Baldwin, E. D. Tomaso","doi":"10.1109/FUZZ.2003.1209437","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209437","url":null,"abstract":"This paper deals with the development of a theory on bayesian networks. It proposes a modified algorithm for solving knowledge querying and information updating, when dealing with continuous variables and with probabilistic and uncertain instantiations. Fuzzy sets are used to rewrite the information contained in a database in order to reduce the complexity of the automatic learning of a bayesian net from data.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"21 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":"131530571","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.2003.1209431
Z. Bien, Dae-Jin Kim, Hyong-Euk Lee, Kwang-Hyun Park, Haiying She, C. Martens, A. Gräser
Human's intention plays a key role in human-machine interaction as in the case of a robot serving for a handicapped person. The quality of a service robot will be much enhanced if the robot can infer the human's intension during the interaction process. In this paper, we propose a soft computing-based technique to read a user's intention using some multisensors-based approach. We have tested the technique by a scenario of 'serving a drink to the user'. With such force/torque or vision sensor, the robot can effectively infer the user's intention to drink the beverage or not to drink. As an application, this intention technique is employed for building a rehabilitation robot, called KARES II, to perform various human-friendly human-robot interaction.
{"title":"Multi sensors-based approach for intention reading with soft computing techniques","authors":"Z. Bien, Dae-Jin Kim, Hyong-Euk Lee, Kwang-Hyun Park, Haiying She, C. Martens, A. Gräser","doi":"10.1109/FUZZ.2003.1209431","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209431","url":null,"abstract":"Human's intention plays a key role in human-machine interaction as in the case of a robot serving for a handicapped person. The quality of a service robot will be much enhanced if the robot can infer the human's intension during the interaction process. In this paper, we propose a soft computing-based technique to read a user's intention using some multisensors-based approach. We have tested the technique by a scenario of 'serving a drink to the user'. With such force/torque or vision sensor, the robot can effectively infer the user's intention to drink the beverage or not to drink. As an application, this intention technique is employed for building a rehabilitation robot, called KARES II, to perform various human-friendly human-robot interaction.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"1 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":"133523050","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.2003.1209350
Ashwani Kumar, D. P. Agrawal, S. Joshi
A method based on genetic algorithm (GA), a simple clustering procedure for rule base generation, and weighted least squares estimation is proposed to construct a Takagi-Sugeno-Kang (TSK) fuzzy inference system directly from numerical data. The rule-base generation method takes the approach of independently clustering input and output spaces, respectively, and assigning a weight to each rule to capture the relation in input-output data. Genetic process learns the number of linguistic terms per variable and the certainty factors of the rules (indirectly the membership-function parameters of the premise part of the fuzzy rules), and the weighted least squares method is used to determine the consequent part of the fuzzy rules. Simulation results on forecasting the stock market and a benchmark case study are included.
{"title":"A GA-based method for constructing TSK fuzzy rules from numerical data","authors":"Ashwani Kumar, D. P. Agrawal, S. Joshi","doi":"10.1109/FUZZ.2003.1209350","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209350","url":null,"abstract":"A method based on genetic algorithm (GA), a simple clustering procedure for rule base generation, and weighted least squares estimation is proposed to construct a Takagi-Sugeno-Kang (TSK) fuzzy inference system directly from numerical data. The rule-base generation method takes the approach of independently clustering input and output spaces, respectively, and assigning a weight to each rule to capture the relation in input-output data. Genetic process learns the number of linguistic terms per variable and the certainty factors of the rules (indirectly the membership-function parameters of the premise part of the fuzzy rules), and the weighted least squares method is used to determine the consequent part of the fuzzy rules. Simulation results on forecasting the stock market and a benchmark case study are included.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"17 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":"133792739","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}
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