Pub Date : 2016-10-01DOI: 10.1109/NAFIPS.2016.7851583
Marc Osswald, Marcel Wehrle, Edy Portmann, Alexander Denzler
Fuzzy graphs (FG) are capable of showing dependencies and relationships between each other to a certain degree. Often, these relationships are described by numbers, which impedes interpretability for humans because they communicate using natural language. This paper seeks to turn the mathematical output of an FG into natural language sentences by applying Restriction-Centered Theory (RCT) to enhance the possibilities of knowledge transfer for humans via an FG. The proposed framework connects FGs and the RCT to produce not only verbalized dependencies but also statements about the dependencies of FGs. As a proof of concept, a use case is introduced, where Swiss Airline's connecting passenger flows are analyzed. The statements of the framework's output are verified by an expert at the company that owns the data.
{"title":"Transforming fuzzy graphs into linguistic variables","authors":"Marc Osswald, Marcel Wehrle, Edy Portmann, Alexander Denzler","doi":"10.1109/NAFIPS.2016.7851583","DOIUrl":"https://doi.org/10.1109/NAFIPS.2016.7851583","url":null,"abstract":"Fuzzy graphs (FG) are capable of showing dependencies and relationships between each other to a certain degree. Often, these relationships are described by numbers, which impedes interpretability for humans because they communicate using natural language. This paper seeks to turn the mathematical output of an FG into natural language sentences by applying Restriction-Centered Theory (RCT) to enhance the possibilities of knowledge transfer for humans via an FG. The proposed framework connects FGs and the RCT to produce not only verbalized dependencies but also statements about the dependencies of FGs. As a proof of concept, a use case is introduced, where Swiss Airline's connecting passenger flows are analyzed. The statements of the framework's output are verified by an expert at the company that owns the data.","PeriodicalId":208265,"journal":{"name":"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131754485","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 : 2016-10-01DOI: 10.1109/NAFIPS.2016.7851604
James A. Crowder
Abduction is formally defined as finding the best explanation for a set of observations, or inferring cause from effect. Here we discuss the notion of Occam Abduction, which relates to finding the simplest explanation with respect to inferring cause from effect. Occam abduction is useful in artificial intelligence in application of autonomous reasoning, knowledge assimilation, belief revision, and works well within a multi-agent AI framework. Here we present a flexible, hypothesis-driven methodology for Occam Abduction within a cognitive, artificially intelligent, system architecture.
{"title":"AI inferences utilizing Occam Abduction","authors":"James A. Crowder","doi":"10.1109/NAFIPS.2016.7851604","DOIUrl":"https://doi.org/10.1109/NAFIPS.2016.7851604","url":null,"abstract":"Abduction is formally defined as finding the best explanation for a set of observations, or inferring cause from effect. Here we discuss the notion of Occam Abduction, which relates to finding the simplest explanation with respect to inferring cause from effect. Occam abduction is useful in artificial intelligence in application of autonomous reasoning, knowledge assimilation, belief revision, and works well within a multi-agent AI framework. Here we present a flexible, hypothesis-driven methodology for Occam Abduction within a cognitive, artificially intelligent, system architecture.","PeriodicalId":208265,"journal":{"name":"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132597488","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 : 2016-10-01DOI: 10.1109/NAFIPS.2016.7851626
B. Lahijanian, M. Zarandi, F. Farahani
In this paper, one of the novel issues of the world regarding the location of ambulance stations within a given area to cover the maximum amount of demand is studied. In this study, the classic version of location problem is improved using the double coverage models so that two radii are considered for covering. Furthermore, the developed study contains the meaningful factors indicating the demand for each patient location covered by each station (vehicle location). In the proposed model, the uncertainty existed in the travel time between the patient locations and vehicle locations have been considered as triangular fuzzy numbers. To solve the proposed model, the goal programming approach is applied in the GAMS software and desired outputs have been achieved. The obtained results represent a significant improvement compared to the past models with uncertainty.
{"title":"Double coverage ambulance location modeling using fuzzy traveling time","authors":"B. Lahijanian, M. Zarandi, F. Farahani","doi":"10.1109/NAFIPS.2016.7851626","DOIUrl":"https://doi.org/10.1109/NAFIPS.2016.7851626","url":null,"abstract":"In this paper, one of the novel issues of the world regarding the location of ambulance stations within a given area to cover the maximum amount of demand is studied. In this study, the classic version of location problem is improved using the double coverage models so that two radii are considered for covering. Furthermore, the developed study contains the meaningful factors indicating the demand for each patient location covered by each station (vehicle location). In the proposed model, the uncertainty existed in the travel time between the patient locations and vehicle locations have been considered as triangular fuzzy numbers. To solve the proposed model, the goal programming approach is applied in the GAMS software and desired outputs have been achieved. The obtained results represent a significant improvement compared to the past models with uncertainty.","PeriodicalId":208265,"journal":{"name":"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128612295","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 : 2016-10-01DOI: 10.1109/NAFIPS.2016.7851589
R. Srinivasan, Syed Siddiqua Begum
In this paper, we introduce the Intuitionistic Fuzzy Sets of Third Type (IFSTT) and study their properties and applications.
本文引入了第三类直觉模糊集(IFSTT)，并研究了它的性质和应用。
{"title":"Properties on Intuitionistic Fuzzy Sets of Third Type","authors":"R. Srinivasan, Syed Siddiqua Begum","doi":"10.1109/NAFIPS.2016.7851589","DOIUrl":"https://doi.org/10.1109/NAFIPS.2016.7851589","url":null,"abstract":"In this paper, we introduce the Intuitionistic Fuzzy Sets of Third Type (IFSTT) and study their properties and applications.","PeriodicalId":208265,"journal":{"name":"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126471048","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 : 2016-10-01DOI: 10.1109/NAFIPS.2016.7851594
Patricia Ochoa, O. Castillo, J. Soria
In this paper we consider the Differential Evolution (DE) algorithm by using fuzzy logic to make dynamic changes in the mutation parameter (F), and this modification of the algorithm we call the Fuzzy Differential Evolution algorithm (FDE). A comparison of the FDE algorithm using type 1 fuzzy logic and interval type-2 fuzzy logic is performed for a set of Benchmark functions.
{"title":"Type-2 fuzzy logic dynamic parameter adaptation in a new Fuzzy Differential Evolution method","authors":"Patricia Ochoa, O. Castillo, J. Soria","doi":"10.1109/NAFIPS.2016.7851594","DOIUrl":"https://doi.org/10.1109/NAFIPS.2016.7851594","url":null,"abstract":"In this paper we consider the Differential Evolution (DE) algorithm by using fuzzy logic to make dynamic changes in the mutation parameter (F), and this modification of the algorithm we call the Fuzzy Differential Evolution algorithm (FDE). A comparison of the FDE algorithm using type 1 fuzzy logic and interval type-2 fuzzy logic is performed for a set of Benchmark functions.","PeriodicalId":208265,"journal":{"name":"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126512398","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 : 2016-10-01DOI: 10.1109/NAFIPS.2016.7851613
F. Cervantes, B. Usevitch, V. Kreinovich
In signal and image processing, it is often beneficial to use semi-heuristic ℓp-methods, i.e., methods that minimize the sum of the p-th powers of the discrepancies. In this paper, we show that a fuzzy-based analysis of the corresponding intuitive idea leads exactly to the ℓp-methods.
{"title":"Why ℓp-methods in signal and image processing: A fuzzy-based explanation","authors":"F. Cervantes, B. Usevitch, V. Kreinovich","doi":"10.1109/NAFIPS.2016.7851613","DOIUrl":"https://doi.org/10.1109/NAFIPS.2016.7851613","url":null,"abstract":"In signal and image processing, it is often beneficial to use semi-heuristic ℓ<inf>p</inf>-methods, i.e., methods that minimize the sum of the p-th powers of the discrepancies. In this paper, we show that a fuzzy-based analysis of the corresponding intuitive idea leads exactly to the ℓ<inf>p</inf>-methods.","PeriodicalId":208265,"journal":{"name":"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128470932","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 : 2016-07-14DOI: 10.1109/NAFIPS.2016.7851629
Shima Soltanzadeh, M. Zarandi, M. B. Astanjin
Diagnosis of male infertility by the laboratory tests is expensive, and sometimes it is intolerable for patients. Filling out the questionnaire and then using classification method can be the first step in decision making process, so only in the cases with a high probability of infertility, we can use the laboratory tests. In this paper, we evaluated the performance of four classification methods including naive Bayesian, neural network, logistic regression, and fuzzy c-means clustering as a classification, in the diagnosis of male infertility due to environmental factors. Since the data are unbalanced, the ROC curves are most suitable method for the comparison. In this paper, we also have selected the more important features using a filtering method and examined the impact of this feature reduction on the performance of each method; generally, most of the methods had better performance after applying the filter. We have showed that using fuzzy c-means clustering as a classification has a good performance according to the ROC curves and its performance is comparable to other classification methods like logistic regression.
{"title":"A hybrid fuzzy clustering approach for fertile and unfertile analysis","authors":"Shima Soltanzadeh, M. Zarandi, M. B. Astanjin","doi":"10.1109/NAFIPS.2016.7851629","DOIUrl":"https://doi.org/10.1109/NAFIPS.2016.7851629","url":null,"abstract":"Diagnosis of male infertility by the laboratory tests is expensive, and sometimes it is intolerable for patients. Filling out the questionnaire and then using classification method can be the first step in decision making process, so only in the cases with a high probability of infertility, we can use the laboratory tests. In this paper, we evaluated the performance of four classification methods including naive Bayesian, neural network, logistic regression, and fuzzy c-means clustering as a classification, in the diagnosis of male infertility due to environmental factors. Since the data are unbalanced, the ROC curves are most suitable method for the comparison. In this paper, we also have selected the more important features using a filtering method and examined the impact of this feature reduction on the performance of each method; generally, most of the methods had better performance after applying the filter. We have showed that using fuzzy c-means clustering as a classification has a good performance according to the ROC curves and its performance is comparable to other classification methods like logistic regression.","PeriodicalId":208265,"journal":{"name":"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124476799","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 : 1900-01-01DOI: 10.1109/nafips.2016.7851576
Marbin Pazos-Revilla, Terry Guo, Motoya Machida, Ernesto León Castro, Ezequiel Avilés Ochoa, J. M. Lindahl, Luis Alessandri Perez Arellano, J. Merigó, Lindahl, R. Hammell, Marcel Wehrle, Edy Portmann
Acceptable product pricing problem using L-localized solutions of max-plus interval linear equations Worrawate Leela-apiradee and Phantipa Thipwiwatpotjana
利用最大+区间线性方程的l -定域解的可接受产品定价问题
{"title":"NAFIPS 2016 - sessions","authors":"Marbin Pazos-Revilla, Terry Guo, Motoya Machida, Ernesto León Castro, Ezequiel Avilés Ochoa, J. M. Lindahl, Luis Alessandri Perez Arellano, J. Merigó, Lindahl, R. Hammell, Marcel Wehrle, Edy Portmann","doi":"10.1109/nafips.2016.7851576","DOIUrl":"https://doi.org/10.1109/nafips.2016.7851576","url":null,"abstract":"Acceptable product pricing problem using L-localized solutions of max-plus interval linear equations Worrawate Leela-apiradee and Phantipa Thipwiwatpotjana","PeriodicalId":208265,"journal":{"name":"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123966596","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 : 1900-01-01DOI: 10.1109/nafips.2016.7851635
Akbar Sadatasl, An Phong, A. Ochoa
{"title":"NAFIPS 2016 - author","authors":"Akbar Sadatasl, An Phong, A. Ochoa","doi":"10.1109/nafips.2016.7851635","DOIUrl":"https://doi.org/10.1109/nafips.2016.7851635","url":null,"abstract":"","PeriodicalId":208265,"journal":{"name":"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133143337","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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