Pub Date : 2011-11-01DOI: 10.1109/ISDA.2011.6121773
Tore Bruland, A. Aamodt, H. Langseth
The overall goal of this research is to study reasoning under uncertainty by combining Bayesian Networks and Case-Based Reasoning through constructing an experimental decision support system for classification of cancer pain. We have experimentally analysed a medical dataset in order to reveal properties of the data with respect to properties of the two reasoning methods. We also preprocessed our medical data with help from a clinical expert, which resulted in four data sets with different characteristics. This culminates in a hybrid system architecture, where CBR handles the exceptions or outliers with respect to the distribution of the data and the target class, while BN handles the more common situations. Through a set of experiments under varying conditions we show that a hybrid BN+CBR system is favorable over each single method.
{"title":"A hybrid CBR and BN architecture refined through data analysis","authors":"Tore Bruland, A. Aamodt, H. Langseth","doi":"10.1109/ISDA.2011.6121773","DOIUrl":"https://doi.org/10.1109/ISDA.2011.6121773","url":null,"abstract":"The overall goal of this research is to study reasoning under uncertainty by combining Bayesian Networks and Case-Based Reasoning through constructing an experimental decision support system for classification of cancer pain. We have experimentally analysed a medical dataset in order to reveal properties of the data with respect to properties of the two reasoning methods. We also preprocessed our medical data with help from a clinical expert, which resulted in four data sets with different characteristics. This culminates in a hybrid system architecture, where CBR handles the exceptions or outliers with respect to the distribution of the data and the target class, while BN handles the more common situations. Through a set of experiments under varying conditions we show that a hybrid BN+CBR system is favorable over each single method.","PeriodicalId":433207,"journal":{"name":"2011 11th International Conference on Intelligent Systems Design and Applications","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124751572","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 : 2011-11-01DOI: 10.1109/ISDA.2011.6121755
D. Kerr, S. Coleman, T. McGinnity, Qingxiang Wu, Marine Clogenson
Inspired by the structure and behaviour of the human visual system, we present an approach to edge detection using spiking neural networks and a biologically plausible hexagonal pixel arrangement. Standard digital images are converted into a hexagonal pixel representation and then processed using a spiking neural network with hexagonal shaped receptive fields. The performance is compared with receptive fields implemented on standard rectangular images. Results illustrate that, using hexagonal shaped receptive fields, performance is improved over standard rectangular shaped receptive fields
{"title":"Biologically inspired edge detection","authors":"D. Kerr, S. Coleman, T. McGinnity, Qingxiang Wu, Marine Clogenson","doi":"10.1109/ISDA.2011.6121755","DOIUrl":"https://doi.org/10.1109/ISDA.2011.6121755","url":null,"abstract":"Inspired by the structure and behaviour of the human visual system, we present an approach to edge detection using spiking neural networks and a biologically plausible hexagonal pixel arrangement. Standard digital images are converted into a hexagonal pixel representation and then processed using a spiking neural network with hexagonal shaped receptive fields. The performance is compared with receptive fields implemented on standard rectangular images. Results illustrate that, using hexagonal shaped receptive fields, performance is improved over standard rectangular shaped receptive fields","PeriodicalId":433207,"journal":{"name":"2011 11th International Conference on Intelligent Systems Design and Applications","volume":"221 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130552716","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 : 2011-11-01DOI: 10.1109/ISDA.2011.6121822
L. M. D. Campos, A. Cano, Francisco Javier García Castellano, S. Moral
In this work, we study the application of Bayesian networks classifiers for gene expression data in three ways: first, we made an exhaustive state-of-art of Bayesian classifiers and Bayesian classifiers induced from microarray data. Second, we propose a preprocessing scheme for gene expression data, to induce Bayesian classifiers. Third, we evaluate different Bayesian classifiers for this kind of data, including the C-RPDAG classifier presented by the authors.
{"title":"Bayesian networks classifiers for gene-expression data","authors":"L. M. D. Campos, A. Cano, Francisco Javier García Castellano, S. Moral","doi":"10.1109/ISDA.2011.6121822","DOIUrl":"https://doi.org/10.1109/ISDA.2011.6121822","url":null,"abstract":"In this work, we study the application of Bayesian networks classifiers for gene expression data in three ways: first, we made an exhaustive state-of-art of Bayesian classifiers and Bayesian classifiers induced from microarray data. Second, we propose a preprocessing scheme for gene expression data, to induce Bayesian classifiers. Third, we evaluate different Bayesian classifiers for this kind of data, including the C-RPDAG classifier presented by the authors.","PeriodicalId":433207,"journal":{"name":"2011 11th International Conference on Intelligent Systems Design and Applications","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121189183","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 : 2011-11-01DOI: 10.1109/ISDA.2011.6121846
P. Ducange, Michela Fazzolari, B. Lazzerini, F. Marcelloni
In this work, an intelligent system for automatic detection of fault in PV fields is proposed. This system is based on a Takagi-Sugeno-Kahn Fuzzy Rule-Based System (TSK-FRBS), which provides an estimation of the instant power production of the PV field in normal functioning, i.e, when no faults occur. Then, the estimated power is compared with the real power and an alarm signal is generated if the difference between powers overcomes a threshold. The TSK-FRBS has been trained using data collected from a PV plant simulator, during normal functioning. Preliminary tests were carried out in a simulated framework, by reproducing both normal and fault conditions. Results show that the system can recognize more than 90% of fault conditions, even when noisy data are introduced.
{"title":"An intelligent system for detecting faults in photovoltaic fields","authors":"P. Ducange, Michela Fazzolari, B. Lazzerini, F. Marcelloni","doi":"10.1109/ISDA.2011.6121846","DOIUrl":"https://doi.org/10.1109/ISDA.2011.6121846","url":null,"abstract":"In this work, an intelligent system for automatic detection of fault in PV fields is proposed. This system is based on a Takagi-Sugeno-Kahn Fuzzy Rule-Based System (TSK-FRBS), which provides an estimation of the instant power production of the PV field in normal functioning, i.e, when no faults occur. Then, the estimated power is compared with the real power and an alarm signal is generated if the difference between powers overcomes a threshold. The TSK-FRBS has been trained using data collected from a PV plant simulator, during normal functioning. Preliminary tests were carried out in a simulated framework, by reproducing both normal and fault conditions. Results show that the system can recognize more than 90% of fault conditions, even when noisy data are introduced.","PeriodicalId":433207,"journal":{"name":"2011 11th International Conference on Intelligent Systems Design and Applications","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114108247","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 : 2011-11-01DOI: 10.1109/ISDA.2011.6121685
M. Aliasghary, I. Eksin, M. Güzelkaya
In this paper, a fuzzy-sliding model reference learning controller is proposed in which optimal scaling factors are assigned for the fuzzy sliding mode controllers. As the name of this study suggest the method is a breeding or hybrid combination of the fuzzy-sliding mode control (FSMC) and fuzzy model reference learning control (FMRLC) which inherits the benefits of these two methods. The main advantage of the proposed controller is that the number of rules has been reduced dramatically in comparison with the traditional FMRLC since fuzzy-sliding mode controllers are invoked in place of standard fuzzy logic controllers. The input and output scaling factors of fuzzy sliding mode controllers are adjusted using big bang - big crunch optimization method to provide an optimal result. The simulations for the proposed method are done on the inverted pendulum system. The results of these simulations demonstrate that the FS-MRLC achieves a robust performance with minimum number of fuzzy rules.
{"title":"Fuzzy-sliding model reference learning control of inverted pendulum with big bang — Big crunch optimization method","authors":"M. Aliasghary, I. Eksin, M. Güzelkaya","doi":"10.1109/ISDA.2011.6121685","DOIUrl":"https://doi.org/10.1109/ISDA.2011.6121685","url":null,"abstract":"In this paper, a fuzzy-sliding model reference learning controller is proposed in which optimal scaling factors are assigned for the fuzzy sliding mode controllers. As the name of this study suggest the method is a breeding or hybrid combination of the fuzzy-sliding mode control (FSMC) and fuzzy model reference learning control (FMRLC) which inherits the benefits of these two methods. The main advantage of the proposed controller is that the number of rules has been reduced dramatically in comparison with the traditional FMRLC since fuzzy-sliding mode controllers are invoked in place of standard fuzzy logic controllers. The input and output scaling factors of fuzzy sliding mode controllers are adjusted using big bang - big crunch optimization method to provide an optimal result. The simulations for the proposed method are done on the inverted pendulum system. The results of these simulations demonstrate that the FS-MRLC achieves a robust performance with minimum number of fuzzy rules.","PeriodicalId":433207,"journal":{"name":"2011 11th International Conference on Intelligent Systems Design and Applications","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116352864","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 : 2011-11-01DOI: 10.1109/ISDA.2011.6121681
A. Smiti, Zied Elouedi
The success of a Case Based Reasoning (CBR) system depends on the quality of case data and the speed of the retrieval process that can be expensive in time especially when the number of cases gets large. To guarantee this quality, maintenance the contents of a case base becomes necessarily. In this paper, we propose a novel case base maintenance (CBM) policy named WCOID - Weighting, Clustering, Outliers and Internal cases Detection, using, in addition to clustering and outliers detection methods, feature weights in the process of improving the competence of our reduced case base. The purpose of our WCOID case base maintenance policy is to reduce both the storage requirements and search time and to focus on balancing case retrieval efficiency and competence for a large size case base. WCOID is mainly based on the idea that a large case base with weighted features is transformed to a small case base.
基于案例的推理(Case Based Reasoning, CBR)系统的成功取决于案例数据的质量和检索过程的速度，这在时间上是昂贵的，特别是当案例数量很大时。为了保证这种质量，维护案例库的内容是必要的。在本文中，我们提出了一种新的案例库维护(CBM)策略，名为WCOID -加权、聚类、异常点和内部案例检测，除了使用聚类和异常点检测方法外，还使用特征权重来提高我们的简化案例库的能力。我们的WCOID案例库维护策略的目的是减少存储需求和搜索时间，并专注于平衡大型案例库的案例检索效率和能力。WCOID主要基于将带有加权特征的大型案例库转换为小型案例库的思想。
{"title":"WCOID: Maintaining case-based reasoning systems using Weighting, Clustering, Outliers and Internal cases Detection","authors":"A. Smiti, Zied Elouedi","doi":"10.1109/ISDA.2011.6121681","DOIUrl":"https://doi.org/10.1109/ISDA.2011.6121681","url":null,"abstract":"The success of a Case Based Reasoning (CBR) system depends on the quality of case data and the speed of the retrieval process that can be expensive in time especially when the number of cases gets large. To guarantee this quality, maintenance the contents of a case base becomes necessarily. In this paper, we propose a novel case base maintenance (CBM) policy named WCOID - Weighting, Clustering, Outliers and Internal cases Detection, using, in addition to clustering and outliers detection methods, feature weights in the process of improving the competence of our reduced case base. The purpose of our WCOID case base maintenance policy is to reduce both the storage requirements and search time and to focus on balancing case retrieval efficiency and competence for a large size case base. WCOID is mainly based on the idea that a large case base with weighted features is transformed to a small case base.","PeriodicalId":433207,"journal":{"name":"2011 11th International Conference on Intelligent Systems Design and Applications","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124297910","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 : 2011-11-01DOI: 10.1109/ISDA.2011.6121830
D. Gómez, J. Montero, J. Yáñez
In this paper we present an efficient hierarchical clustering algorithm for relational data, being those relations modeled by a graph. The hierarchical clustering approach proposed in this paper is based on divisive and link criteria, to break the graph and join the nodes at different stages. We then apply this approach to a community detection problems based on the well-known edge line betweenness measure as the divisive criterium and a fuzzy similarity relation as the link criterium. We present also some computational results in some well-known examples like the Karate Zachary club-network, the Dolphins network, Les Miserables network and the Authors centrality network, comparing these results to some standard methodologies for hierarchical clustering problem, both for binary and valued graphs.
{"title":"A divide-link algorithm based on fuzzy similarity for clustering networks","authors":"D. Gómez, J. Montero, J. Yáñez","doi":"10.1109/ISDA.2011.6121830","DOIUrl":"https://doi.org/10.1109/ISDA.2011.6121830","url":null,"abstract":"In this paper we present an efficient hierarchical clustering algorithm for relational data, being those relations modeled by a graph. The hierarchical clustering approach proposed in this paper is based on divisive and link criteria, to break the graph and join the nodes at different stages. We then apply this approach to a community detection problems based on the well-known edge line betweenness measure as the divisive criterium and a fuzzy similarity relation as the link criterium. We present also some computational results in some well-known examples like the Karate Zachary club-network, the Dolphins network, Les Miserables network and the Authors centrality network, comparing these results to some standard methodologies for hierarchical clustering problem, both for binary and valued graphs.","PeriodicalId":433207,"journal":{"name":"2011 11th International Conference on Intelligent Systems Design and Applications","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124035896","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 : 2011-11-01DOI: 10.1109/ISDA.2011.6121787
M. Satler, Christian Vidal-Castro, F. P. Romero, J. A. Olivas, J. L. Braga
Nowadays, the impact of e-Learning developments on improving educational activities is becoming more evident. The Portfolio approach has emerged as important alternative to increase the learning process. However, most of tools to support portfolio assessment are far from to come up to the expectations. In this work we propose a fuzzy ontology-based framework to support portfolio assessment. Our approach focuses on portfolio semantic representation and conceptual matching to generate a portfolio evaluation report, which helps teachers in portfolio assessment tasks. The initial experiments results indicate that the approach is useful and warrants further research.
{"title":"Enhancing portfolio assessment: An application of fuzzy ontologies","authors":"M. Satler, Christian Vidal-Castro, F. P. Romero, J. A. Olivas, J. L. Braga","doi":"10.1109/ISDA.2011.6121787","DOIUrl":"https://doi.org/10.1109/ISDA.2011.6121787","url":null,"abstract":"Nowadays, the impact of e-Learning developments on improving educational activities is becoming more evident. The Portfolio approach has emerged as important alternative to increase the learning process. However, most of tools to support portfolio assessment are far from to come up to the expectations. In this work we propose a fuzzy ontology-based framework to support portfolio assessment. Our approach focuses on portfolio semantic representation and conceptual matching to generate a portfolio evaluation report, which helps teachers in portfolio assessment tasks. The initial experiments results indicate that the approach is useful and warrants further research.","PeriodicalId":433207,"journal":{"name":"2011 11th International Conference on Intelligent Systems Design and Applications","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126454766","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 : 2011-11-01DOI: 10.1109/ISDA.2011.6121753
R. Domínguez, E. Onieva, J. Alonso, J. Villagrá, Carlos González
In this work, a solution for clustering and tracking obstacles in the area covered by a LIDAR sensor is presented. It is based on a combination of simple artificial intelligence techniques and it is conceived as an initial version of a detection and tracking system for objects of any shape that an autonomous vehicle might find in its surroundings. The proposed solution divides the problem into three consecutive phases: 1) segmentation, 2) fragmentation detection and clustering and 3) tracking. The work done has been tested with real world LIDAR scan samples taken from an instrumented vehicle.
{"title":"LIDAR based perception solution for autonomous vehicles","authors":"R. Domínguez, E. Onieva, J. Alonso, J. Villagrá, Carlos González","doi":"10.1109/ISDA.2011.6121753","DOIUrl":"https://doi.org/10.1109/ISDA.2011.6121753","url":null,"abstract":"In this work, a solution for clustering and tracking obstacles in the area covered by a LIDAR sensor is presented. It is based on a combination of simple artificial intelligence techniques and it is conceived as an initial version of a detection and tracking system for objects of any shape that an autonomous vehicle might find in its surroundings. The proposed solution divides the problem into three consecutive phases: 1) segmentation, 2) fragmentation detection and clustering and 3) tracking. The work done has been tested with real world LIDAR scan samples taken from an instrumented vehicle.","PeriodicalId":433207,"journal":{"name":"2011 11th International Conference on Intelligent Systems Design and Applications","volume":"339 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133912152","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 : 2011-11-01DOI: 10.1109/ISDA.2011.6121664
L. Herrera, Antonio Mora García, C. Fernandes, D. Migotina, A. Guillén, A. Rosa
Manual visualization-based sleep stage classification is a time-consuming task prone to errors. Since the correct identification of sleep stages is vital for the correct identification of sleep disorders and for the research in this field in general, there is a growing demand for efficient automatic classification methods. However, there is still no symbolic representation of the biomedical signals that leads to a reliable and accurate automatic sleep classification system. This work presents the application of a novel method for symbolic representation of the EEG and evaluates its potential as information source for a sleep stage classifier, in this case a SVM classifier. The data is first analyzed using Self-Organizing Maps (SOM) and a mutual information (MI)-based variable selection algorithm. Preliminary results of sleep data classification provide success rates around 70%. These results are promising since only EEG is used, and there is still room for improvement in this new symbolic representation of the signal.
{"title":"Symbolic representation of the EEG for sleep stage classification","authors":"L. Herrera, Antonio Mora García, C. Fernandes, D. Migotina, A. Guillén, A. Rosa","doi":"10.1109/ISDA.2011.6121664","DOIUrl":"https://doi.org/10.1109/ISDA.2011.6121664","url":null,"abstract":"Manual visualization-based sleep stage classification is a time-consuming task prone to errors. Since the correct identification of sleep stages is vital for the correct identification of sleep disorders and for the research in this field in general, there is a growing demand for efficient automatic classification methods. However, there is still no symbolic representation of the biomedical signals that leads to a reliable and accurate automatic sleep classification system. This work presents the application of a novel method for symbolic representation of the EEG and evaluates its potential as information source for a sleep stage classifier, in this case a SVM classifier. The data is first analyzed using Self-Organizing Maps (SOM) and a mutual information (MI)-based variable selection algorithm. Preliminary results of sleep data classification provide success rates around 70%. These results are promising since only EEG is used, and there is still room for improvement in this new symbolic representation of the signal.","PeriodicalId":433207,"journal":{"name":"2011 11th International Conference on Intelligent Systems Design and Applications","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130989642","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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