Structural similarity computation plays a crucial role in many applications such as in searching similar documents, in comparing chemical compounds, in finding genetic similarities, etc. We propose in this paper to use structural information content (SIC) for measuring structural information, considering both the nodes and edges of trees. We utilize a binary encoding approach for assigning the weights of different layer nodes and determining if some tree is a subtree of another tree. By defining a fast kernel and recursively computing SICs, we evaluate the structural information similarities of data trees to pattern trees. In the paper, we present the algorithm for calculating SICs with computation complexity of O(n), and use simple examples to instantiate the performance of the proposed method
{"title":"Fast Kernel for Calculating Structural Information Similarities","authors":"Jinmao Wei, Shuqin Wang, Jing Wang, Junping You","doi":"10.1109/IS.2006.348394","DOIUrl":"https://doi.org/10.1109/IS.2006.348394","url":null,"abstract":"Structural similarity computation plays a crucial role in many applications such as in searching similar documents, in comparing chemical compounds, in finding genetic similarities, etc. We propose in this paper to use structural information content (SIC) for measuring structural information, considering both the nodes and edges of trees. We utilize a binary encoding approach for assigning the weights of different layer nodes and determining if some tree is a subtree of another tree. By defining a fast kernel and recursively computing SICs, we evaluate the structural information similarities of data trees to pattern trees. In the paper, we present the algorithm for calculating SICs with computation complexity of O(n), and use simple examples to instantiate the performance of the proposed method","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124544992","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}
The public pension system in Japan is bringing about intergenerational inequity in contribution/benefit ratio. In this paper, we propose a scheme for public pension planning under generation-based operation (GO) model. In addition, to show sustainability of the proposed scheme, we simulate processes in finance. Proposed scheme is robust for various disturbances (e.g. changes in population and economy). The robustness to changes in population is especially useful in society with a decreasing population
{"title":"On Planning a Public Pension System under Uncertainty: A Generation-based Operation Model","authors":"D. Banjo, H. Tamura, T. Murata","doi":"10.1109/IS.2006.348409","DOIUrl":"https://doi.org/10.1109/IS.2006.348409","url":null,"abstract":"The public pension system in Japan is bringing about intergenerational inequity in contribution/benefit ratio. In this paper, we propose a scheme for public pension planning under generation-based operation (GO) model. In addition, to show sustainability of the proposed scheme, we simulate processes in finance. Proposed scheme is robust for various disturbances (e.g. changes in population and economy). The robustness to changes in population is especially useful in society with a decreasing population","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126748221","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}
System identification plays a principal role in input-output data analysis, such that a better result can be obtained from better model. System identification includes two parts: structure identification and parameter identification. In structure identification, input variables and input-output relations are found. This paper tries to find best input candidate for a TSK fuzzy identification model based on modified mountain clustering
{"title":"Input Selection for TSK Fuzzy Model based on Modified Mountain Clustering","authors":"A. Banakar, M. Azeem","doi":"10.1109/IS.2006.348434","DOIUrl":"https://doi.org/10.1109/IS.2006.348434","url":null,"abstract":"System identification plays a principal role in input-output data analysis, such that a better result can be obtained from better model. System identification includes two parts: structure identification and parameter identification. In structure identification, input variables and input-output relations are found. This paper tries to find best input candidate for a TSK fuzzy identification model based on modified mountain clustering","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"220 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122253746","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 : 2006-09-01DOI: 10.1007/978-3-540-77623-9_7
H. Bustince, E. Tartas, M. Pagola
{"title":"A method for constructing V. Young's fuzzy subsethood measures and fuzzy entropies","authors":"H. Bustince, E. Tartas, M. Pagola","doi":"10.1007/978-3-540-77623-9_7","DOIUrl":"https://doi.org/10.1007/978-3-540-77623-9_7","url":null,"abstract":"","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133556878","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}
The process management spectrum extends from conventional workflow processes to emergent processes. Three categories of process are identified. Activity-driven processes that are managed by a single reactive agent architecture. Goal-driven processes that are managed by a multiagent system of deliberative agents. Knowledge-driven processes that are managed by augmenting the multiagent system from the goal-driven system with an approach based on task types. The idea behind task types is that if the system knows what sort of task is being worked on by the (human) users then appropriate support may be provided. Three general purpose agent architectures are described, one for each category of process. The business of process management is generally limited to the management of the processes themselves - this is appropriate for production workflows. Goal-driven and knowledge-driven processes both rely on the management of the collaboration between the human players. Collaboration management is seen here to be an important component of process management, and an agent architecture, founded on concepts from information theory, is described for it
{"title":"Intelligent Agents that Span the Process Management Spectrum","authors":"J. Debenham, S. Simoff","doi":"10.1109/IS.2006.348450","DOIUrl":"https://doi.org/10.1109/IS.2006.348450","url":null,"abstract":"The process management spectrum extends from conventional workflow processes to emergent processes. Three categories of process are identified. Activity-driven processes that are managed by a single reactive agent architecture. Goal-driven processes that are managed by a multiagent system of deliberative agents. Knowledge-driven processes that are managed by augmenting the multiagent system from the goal-driven system with an approach based on task types. The idea behind task types is that if the system knows what sort of task is being worked on by the (human) users then appropriate support may be provided. Three general purpose agent architectures are described, one for each category of process. The business of process management is generally limited to the management of the processes themselves - this is appropriate for production workflows. Goal-driven and knowledge-driven processes both rely on the management of the collaboration between the human players. Collaboration management is seen here to be an important component of process management, and an agent architecture, founded on concepts from information theory, is described for it","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132558062","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}
This paper presents a new development of a rough-fuzzy controller for an autonomous mobile robot based on rough set and fuzzy set theory. It has been tested in different environments with the Saphira simulation software. The proposed approach provides an improvement in uncertainty reasoning by using a rough-fuzzy controller, resulting in better wall-following behavior performance as compared against other controllers. The rough-fuzziness of the input data leads to the enhanced uncertainty reasoning process by calculating the roughly approximated fuzzified value of the input, which makes the system more robust and reliable
{"title":"A Rough-Fuzzy Controller for Autonomous Mobile Robot Navigation","authors":"Chang Su Lee, T. Braunl, A. Zaknich","doi":"10.1109/IS.2006.348501","DOIUrl":"https://doi.org/10.1109/IS.2006.348501","url":null,"abstract":"This paper presents a new development of a rough-fuzzy controller for an autonomous mobile robot based on rough set and fuzzy set theory. It has been tested in different environments with the Saphira simulation software. The proposed approach provides an improvement in uncertainty reasoning by using a rough-fuzzy controller, resulting in better wall-following behavior performance as compared against other controllers. The rough-fuzziness of the input data leads to the enhanced uncertainty reasoning process by calculating the roughly approximated fuzzified value of the input, which makes the system more robust and reliable","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131676353","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}
Businesses collect and keep large volumes of customer data as part of their processes. Analysis of this data by business users often leads to discovery of valuable patterns and trends that otherwise would go unnoticed and that can lead to prioritization of decisions on future investments. The majority of tools currently available to business users are typically limited to computing summary statistics, simple visualization and reporting of data. More complex tools that could offer possible explanations for observations, discover knowledge, or allow making predictions are usually aimed at an academic audience or at users who are highly trained in analytics. However, it is business users with little experience in analytics who require access to tools that allow them to easily model customer behavior and build future scenarios. In this paper we present a tool we developed for business users to perform advanced analysis on customer data
{"title":"A Tool for Intelligent Customer Analytics","authors":"D. Nauck, D. Ruta, M. Spott, B. Azvine","doi":"10.1109/IS.2006.348473","DOIUrl":"https://doi.org/10.1109/IS.2006.348473","url":null,"abstract":"Businesses collect and keep large volumes of customer data as part of their processes. Analysis of this data by business users often leads to discovery of valuable patterns and trends that otherwise would go unnoticed and that can lead to prioritization of decisions on future investments. The majority of tools currently available to business users are typically limited to computing summary statistics, simple visualization and reporting of data. More complex tools that could offer possible explanations for observations, discover knowledge, or allow making predictions are usually aimed at an academic audience or at users who are highly trained in analytics. However, it is business users with little experience in analytics who require access to tools that allow them to easily model customer behavior and build future scenarios. In this paper we present a tool we developed for business users to perform advanced analysis on customer data","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132992646","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}
D. Peneva, V. Tasseva, V. Kodogiannis, E. Sotirova, K. Atanassov
The development of an expert system is a parallel process, involving the cycle of knowledge acquisition and representation, programming, testing, verification and validation of results, and so on. Generalized net models have been developed that describe the process of functioning and machine learning of expert systems. With the aid of GN, some ways for presenting the functioning and results of an ES from the type of rule-based production system are described. In this paper a reduced GN is used for process of expert system construction representation. The GN-model includes methodology for expert system development as well as interactions between the participants in the process
{"title":"Generalized Nets as an Instrument for Description of the Process of Expert System Construction","authors":"D. Peneva, V. Tasseva, V. Kodogiannis, E. Sotirova, K. Atanassov","doi":"10.1109/IS.2006.348515","DOIUrl":"https://doi.org/10.1109/IS.2006.348515","url":null,"abstract":"The development of an expert system is a parallel process, involving the cycle of knowledge acquisition and representation, programming, testing, verification and validation of results, and so on. Generalized net models have been developed that describe the process of functioning and machine learning of expert systems. With the aid of GN, some ways for presenting the functioning and results of an ES from the type of rule-based production system are described. In this paper a reduced GN is used for process of expert system construction representation. The GN-model includes methodology for expert system development as well as interactions between the participants in the process","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"468 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133004538","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}
A. Solanas, A. Martínez-Ballesté, J. M. Mateo-Sanz, J. Domingo-Ferrer
Microaggregation is a clustering problem with cardinality constraints that originated in the area of statistical disclosure control for micro data. This article presents a method for multivariate microaggregation based on genetic algorithms (GA). The adaptations required to characterize the multivariate microaggregation problem are explained and justified. Extensive experimentation has been carried out with the aim of finding the best values for the most relevant parameters of the modified GA: the population size and the crossover and mutation rates. The experimental results demonstrate that our method finds the optimal solution to the problem in almost all experiments when working with small data sets. Thus, for small data sets the proposed method performs better than known polynomial heuristics and can be combined with these for larger data sets. Moreover, a sensitivity analysis of parameter values is reported which shows the influence of the parameters and their best values
{"title":"Multivariate Microaggregation Based Genetic Algorithms","authors":"A. Solanas, A. Martínez-Ballesté, J. M. Mateo-Sanz, J. Domingo-Ferrer","doi":"10.1109/IS.2006.348395","DOIUrl":"https://doi.org/10.1109/IS.2006.348395","url":null,"abstract":"Microaggregation is a clustering problem with cardinality constraints that originated in the area of statistical disclosure control for micro data. This article presents a method for multivariate microaggregation based on genetic algorithms (GA). The adaptations required to characterize the multivariate microaggregation problem are explained and justified. Extensive experimentation has been carried out with the aim of finding the best values for the most relevant parameters of the modified GA: the population size and the crossover and mutation rates. The experimental results demonstrate that our method finds the optimal solution to the problem in almost all experiments when working with small data sets. Thus, for small data sets the proposed method performs better than known polynomial heuristics and can be combined with these for larger data sets. Moreover, a sensitivity analysis of parameter values is reported which shows the influence of the parameters and their best values","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132298043","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}
Many spatial association rule mining algorithms have been developed to extract interesting patterns from large geographic databases. However, a large amount of knowledge explicitly represented in geographic database schemas has not been used to reduce the number of association rules. A significant number of well known dependences, explicitly represented by the database designer, are unnecessarily extracted by association rule mining algorithms. The result is the generation of hundreds or thousands of well known spatial association rules. This paper presents an approach for mining spatial association rules where both database and schema are considered. We propose the APRIORI-KC (a priori knowledge constraints) algorithm to eliminate all associations explicitly represented in geographic database schemas. Experiments show a very significant reduction of the number of rules and the elimination of well known rules
{"title":"Towards Elimination of Well Known Geographic Patterns in Spatial Association Rule Mining","authors":"V. Bogorny, S. Camargo, P. Engel, L. Alvares","doi":"10.1109/IS.2006.348476","DOIUrl":"https://doi.org/10.1109/IS.2006.348476","url":null,"abstract":"Many spatial association rule mining algorithms have been developed to extract interesting patterns from large geographic databases. However, a large amount of knowledge explicitly represented in geographic database schemas has not been used to reduce the number of association rules. A significant number of well known dependences, explicitly represented by the database designer, are unnecessarily extracted by association rule mining algorithms. The result is the generation of hundreds or thousands of well known spatial association rules. This paper presents an approach for mining spatial association rules where both database and schema are considered. We propose the APRIORI-KC (a priori knowledge constraints) algorithm to eliminate all associations explicitly represented in geographic database schemas. Experiments show a very significant reduction of the number of rules and the elimination of well known rules","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123905816","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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