Peng Liu, Lei Lei, Junjie Yin, Wei Zhang, Wu Naijun, E. El-Darzi
Data mining approaches have been widely applied in the field of healthcare. At the same time it is recognized that most healthcare datasets are full of missing values. In this paper we apply decision trees, Naive Bayesian classifiers and feature selection methods to a geriatric hospital dataset in order to predict inpatient length of stay, especially for the long stay patients
{"title":"Healthcare Data Mining: Prediction Inpatient Length of Stay","authors":"Peng Liu, Lei Lei, Junjie Yin, Wei Zhang, Wu Naijun, E. El-Darzi","doi":"10.1109/IS.2006.348528","DOIUrl":"https://doi.org/10.1109/IS.2006.348528","url":null,"abstract":"Data mining approaches have been widely applied in the field of healthcare. At the same time it is recognized that most healthcare datasets are full of missing values. In this paper we apply decision trees, Naive Bayesian classifiers and feature selection methods to a geriatric hospital dataset in order to predict inpatient length of stay, especially for the long stay patients","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"23 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":"124952234","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 discusses an evaluation method of fuzzy numbers/fuzzy random variables by mean values and variance defined by fuzzy measures, and the method is applicable to decision making with both randomness and fuzziness. Next, we compare several possible approaches regarding variances by examining them for some fuzzy random variables with values at triangle-type fuzzy numbers. We find the method with lambda-mean functions has proper properties, and we derive fundamental properties regarding the variance and the corresponding co-variance and correlation. Formulae are given to apply the results to triangle-type fuzzy numbers, trapezoidal-type fuzzy numbers, and some types of fuzzy random variables
{"title":"Mean Value and Variance of Fuzzy Random Variables by Evaluation Measures","authors":"Y. Yoshida","doi":"10.1109/IS.2006.348423","DOIUrl":"https://doi.org/10.1109/IS.2006.348423","url":null,"abstract":"This paper discusses an evaluation method of fuzzy numbers/fuzzy random variables by mean values and variance defined by fuzzy measures, and the method is applicable to decision making with both randomness and fuzziness. Next, we compare several possible approaches regarding variances by examining them for some fuzzy random variables with values at triangle-type fuzzy numbers. We find the method with lambda-mean functions has proper properties, and we derive fundamental properties regarding the variance and the corresponding co-variance and correlation. Formulae are given to apply the results to triangle-type fuzzy numbers, trapezoidal-type fuzzy numbers, and some types of fuzzy random variables","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"45 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":"123280615","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_4
P. Bosc, N. I. Hssaien, O. Pivert
{"title":"On the evaluation of cardinality-based generalized yes/no queries","authors":"P. Bosc, N. I. Hssaien, O. Pivert","doi":"10.1007/978-3-540-77623-9_4","DOIUrl":"https://doi.org/10.1007/978-3-540-77623-9_4","url":null,"abstract":"","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"44 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":"121586380","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 low error rate of naive Bayes (NB) classifier has been described as surprising. It is known that class conditional independence of the features is sufficient but not a necessary condition for optimality of NB. This study is about the difference between the estimated error and the true error of NB taking into account feature dependencies. Analytical results are derived for two binary features. Illustration examples are also provided
{"title":"Naive Bayes classifier: True and estimated errors for 2-class, 2-features case","authors":"Z. Hoare","doi":"10.1109/IS.2006.348481","DOIUrl":"https://doi.org/10.1109/IS.2006.348481","url":null,"abstract":"The low error rate of naive Bayes (NB) classifier has been described as surprising. It is known that class conditional independence of the features is sufficient but not a necessary condition for optimality of NB. This study is about the difference between the estimated error and the true error of NB taking into account feature dependencies. Analytical results are derived for two binary features. Illustration examples are also provided","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"27 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":"121595407","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 the application of genetic algorithms (GAs) to the design of an intelligent switching surface for variable structure adaptive model following controller for higher order systems with unmodelled dynamics/parameter variations. The conventional approach for the design of switching surface by pole placement method often lead to large value of control signals. A method for obtaining an intelligent switching surface in a computationally efficient manner is proposed in this paper. The proposed method make use of GAs to evolve a switching surface which ensures minimum disruption of the poles when variations/uncertainties act on the system. If minimum disruption of the poles is not ensured, higher control signal will be required to maintain sliding mode motion. The proposed methodology is applied to a practical system namely a flexible one-link manipulator and the results obtained are compared to the results obtained by applying the conventional design. The comparison reveals the efficacy of the proposed method
{"title":"Intelligent Switching Surface for Variable Structure Adaptive Model Following Control","authors":"S. Thomas, H. Reddy","doi":"10.1109/IS.2006.348437","DOIUrl":"https://doi.org/10.1109/IS.2006.348437","url":null,"abstract":"This paper presents the application of genetic algorithms (GAs) to the design of an intelligent switching surface for variable structure adaptive model following controller for higher order systems with unmodelled dynamics/parameter variations. The conventional approach for the design of switching surface by pole placement method often lead to large value of control signals. A method for obtaining an intelligent switching surface in a computationally efficient manner is proposed in this paper. The proposed method make use of GAs to evolve a switching surface which ensures minimum disruption of the poles when variations/uncertainties act on the system. If minimum disruption of the poles is not ensured, higher control signal will be required to maintain sliding mode motion. The proposed methodology is applied to a practical system namely a flexible one-link manipulator and the results obtained are compared to the results obtained by applying the conventional design. The comparison reveals the efficacy of the proposed method","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"82 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":"122161146","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}
Differently from pure probability theory the common uncertain information is perception-based and imprecise (L.A. Zadeh, 2002). Human belief, confidence level, etc., are approximate human perceptions and the intelligent systems need a general approximate reasoning logic for them. We propose a family of intuitionistic bilattices with full truth-knowledge duality to be used in logic programming for such uncertain information. The simplest of them, based on intuitionistic truth-functually complete extension of Belnap's 4-valued bilattice, can be used in paraconsistent programming, that is, for knowledge bases with incomplete and inconsistent information. The other two families are useful for an approximate logic theory where the uncertainty in the knowledge about a piece of information is in the form of human granulation cognition types: as an interval-probability belief or as a confidence level. Such logic programs can be parameterized by different kinds of probabilistic conjunctive/disjunctive strategies for their rules, based on intuitionistic implication, which express the user perception-based correlation between observed knowledge facts
{"title":"Intuitionistic Truth-Knowledge Symmetric Bilattices for Uncertainty in Intel1igent systems","authors":"Z. Majkic","doi":"10.1109/IS.2006.348505","DOIUrl":"https://doi.org/10.1109/IS.2006.348505","url":null,"abstract":"Differently from pure probability theory the common uncertain information is perception-based and imprecise (L.A. Zadeh, 2002). Human belief, confidence level, etc., are approximate human perceptions and the intelligent systems need a general approximate reasoning logic for them. We propose a family of intuitionistic bilattices with full truth-knowledge duality to be used in logic programming for such uncertain information. The simplest of them, based on intuitionistic truth-functually complete extension of Belnap's 4-valued bilattice, can be used in paraconsistent programming, that is, for knowledge bases with incomplete and inconsistent information. The other two families are useful for an approximate logic theory where the uncertainty in the knowledge about a piece of information is in the form of human granulation cognition types: as an interval-probability belief or as a confidence level. Such logic programs can be parameterized by different kinds of probabilistic conjunctive/disjunctive strategies for their rules, based on intuitionistic implication, which express the user perception-based correlation between observed knowledge facts","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":"131139774","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}
In this paper we present PACE - a probabilistic ant based clustering algorithm for distributed databases. This algorithm is based on the well-known swarm based approach to clustering. Its characteristic feature is the formation of numerous zones in various distributed sites based on the user query to the distributed database. Keywords, extracted out of the query, are used to assign a range of values according to their corresponding probability of occurrence or hit ratio at each site. An ant odor identification model is used as a preceding step to the colony building and formation of clusters inside the zones. Reordering or sorting of the heap trees formed by the ants to enable agglomeration of only the most probable data forms the crux of this algorithm. Experimental results are reported showing the comparison of PACE with other existing clustering algorithms
{"title":"Probabilistic Ant based Clustering for Distributed Databases","authors":"R. Chandrasekar, V. Vijaykumar, T. Srinivasan","doi":"10.1109/IS.2006.348477","DOIUrl":"https://doi.org/10.1109/IS.2006.348477","url":null,"abstract":"In this paper we present PACE - a probabilistic ant based clustering algorithm for distributed databases. This algorithm is based on the well-known swarm based approach to clustering. Its characteristic feature is the formation of numerous zones in various distributed sites based on the user query to the distributed database. Keywords, extracted out of the query, are used to assign a range of values according to their corresponding probability of occurrence or hit ratio at each site. An ant odor identification model is used as a preceding step to the colony building and formation of clusters inside the zones. Reordering or sorting of the heap trees formed by the ants to enable agglomeration of only the most probable data forms the crux of this algorithm. Experimental results are reported showing the comparison of PACE with other existing clustering algorithms","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"74 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":"121321048","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}
In this paper, a decision support system based on rough sets and a probabilistic neural network is presented. Rough sets were employed as they have the capacity to reduce the dimensionality of the dataset and also produce a set of readily understandable rules. A probabilistic neural network was also employed to classify this dataset, comparing the classification accuracy to that obtained with rough sets. We firstly evaluate the effectiveness of these machine learning algorithms on a real-life small biomedical dataset. The classification results indicate that both classifiers produce a high level of accuracy (87% or better). The rough sets algorithm produced a set of rules that are readily interpretable by a domain expert. The PNN algorithm produced a classifier that was robust to noise and missing values. These preliminary results indicate that the both rough sets and PNN machine learning approaches can be successfully applied synergistically to biomedical datasets that contain a variety of attribute types, missing values and multiple decision classes
{"title":"Mining A Primary Biliary Cirrhosis Dataset Using Rough Sets and a Probabilistic Neural Network","authors":"K. Revett, F. Gorunescu, M. Gorunescu, M. Ene","doi":"10.1109/IS.2006.348432","DOIUrl":"https://doi.org/10.1109/IS.2006.348432","url":null,"abstract":"In this paper, a decision support system based on rough sets and a probabilistic neural network is presented. Rough sets were employed as they have the capacity to reduce the dimensionality of the dataset and also produce a set of readily understandable rules. A probabilistic neural network was also employed to classify this dataset, comparing the classification accuracy to that obtained with rough sets. We firstly evaluate the effectiveness of these machine learning algorithms on a real-life small biomedical dataset. The classification results indicate that both classifiers produce a high level of accuracy (87% or better). The rough sets algorithm produced a set of rules that are readily interpretable by a domain expert. The PNN algorithm produced a classifier that was robust to noise and missing values. These preliminary results indicate that the both rough sets and PNN machine learning approaches can be successfully applied synergistically to biomedical datasets that contain a variety of attribute types, missing values and multiple decision classes","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"3 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":"121250074","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 an InfoStation-based multi-agent system, which provides intelligent mobile services in a University campus area. The corresponding network architecture (both horizontally and vertically) is presented. A description of some of the intelligent mobile services along with interaction among sample entities is provided. Technologies for delivering of these services are discussed, and approaches for the system implementation and structuring are considered
{"title":"An InfoStation-Based Multi-Agent System for the Provision of Intelligent Mobile Services in a University Campus Area","authors":"Ivan Ganchev, S. Stojanov, M. O'Droma, D. Meere","doi":"10.1109/IS.2006.348457","DOIUrl":"https://doi.org/10.1109/IS.2006.348457","url":null,"abstract":"This paper presents an InfoStation-based multi-agent system, which provides intelligent mobile services in a University campus area. The corresponding network architecture (both horizontally and vertically) is presented. A description of some of the intelligent mobile services along with interaction among sample entities is provided. Technologies for delivering of these services are discussed, and approaches for the system implementation and structuring are considered","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"26 2 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":"125681573","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}
Prostate cancer remains one of the leading causes of cancer death worldwide, with a reported incidence rate of 650,000 cases per annum worldwide. The causal factors of prostate cancer still remain to be determined. In this paper, we investigate a medical dataset containing clinical information on 502 prostate cancer patients using the machine learning technique of rough sets. Our preliminary results yield a classification accuracy of 90%, with high sensitivity and specificity (both at approximately 91%). Our results yield a predictive positive value (PPN) of 81% and a predictive negative value (PNV) of 95%. In addition to the high classification accuracy of our system, the rough set approach also provides a rule-based inference mechanism for information extraction that is suitable for integration into a rule-based system. The generated rules relate directly to the attributes and their values and provide a direct mapping between them
{"title":"Data Mining a Prostate Cancer Dataset Using Rough Sets","authors":"K. Revett, S.T. de Magalhaes, H. Santos","doi":"10.1109/IS.2006.348433","DOIUrl":"https://doi.org/10.1109/IS.2006.348433","url":null,"abstract":"Prostate cancer remains one of the leading causes of cancer death worldwide, with a reported incidence rate of 650,000 cases per annum worldwide. The causal factors of prostate cancer still remain to be determined. In this paper, we investigate a medical dataset containing clinical information on 502 prostate cancer patients using the machine learning technique of rough sets. Our preliminary results yield a classification accuracy of 90%, with high sensitivity and specificity (both at approximately 91%). Our results yield a predictive positive value (PPN) of 81% and a predictive negative value (PNV) of 95%. In addition to the high classification accuracy of our system, the rough set approach also provides a rule-based inference mechanism for information extraction that is suitable for integration into a rule-based system. The generated rules relate directly to the attributes and their values and provide a direct mapping between them","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"10 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":"126077852","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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