The Experts below are selected from a list of 78 Experts worldwide ranked by ideXlab platform
Takahira Yamaguchi - One of the best experts on this subject based on the ideXlab platform.
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PKAW - Evaluating learning algorithms with meta-learning schemes for a rule evaluation support method based on objective indices
Advances in Knowledge Acquisition and Management, 2006Co-Authors: Hidenao Abe, Shusaku Tsumoto, Miho Ohsaki, Hideto Yokoi, Takahira YamaguchiAbstract:In this paper, we present evaluations of learning algorithms for a novel rule evaluation support method in Data Mining post-processing, which is one of the key processes in a Data Mining process. It is difficult for human experts to evaluate many thousands of rules from a large Dataset with noises completely. To reduce the costs of rule evaluation task, we have developed the rule evaluation support method with rule evaluation models, which are learned from a Dataset consisted of objective indices and evaluations of a human expert for each rule. To enhance adaptability of rule evaluation models, we introduced a constructive meta-learning system to choose proper learning algorithms for constructing them. Then, we have done a case study on the meningitis Data Mining Result, the hepatitis Data Mining Results and rule sets from the eight UCI Datasets.
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evaluating a rule evaluation support method with learning models based on objective rule evaluation indices a case study with a meningitis Data Mining Result
International Conference Hybrid Intelligent Systems, 2005Co-Authors: Hidenao Abe, Shusaku Tsumoto, Miho Ohsaki, Takahira YamaguchiAbstract:In this paper, we present a novel rule evaluation support method for post-processing of mined Results with rule evaluation models based on objective indices. Post-processing of mined Results is one of the key issues to make a Data Mining process successfully. However, it is difficult for human experts to evaluate many thousands of rules from a large Dataset with noises completely. To reduce the costs of rule evaluation procedures, we have developed the rule evaluation support method with rule evaluation models, which are obtained with objective indices of mined classification rules and evaluations of a human expert for each rule. To evaluate performances of learning algorithms for constructing rule evaluation models, we have done a case study on the meningitis Data Mining as an actual problem. Then we discuss the availability of our rule evaluation support method.
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HIS - Evaluating a rule evaluation support method with learning models based on objective rule evaluation indices - a case study with a meningitis Data Mining Result
Fifth International Conference on Hybrid Intelligent Systems (HIS'05), 2005Co-Authors: Hidenao Abe, Shusaku Tsumoto, Miho Ohsaki, Takahira YamaguchiAbstract:In this paper, we present a novel rule evaluation support method for post-processing of mined Results with rule evaluation models based on objective indices. Post-processing of mined Results is one of the key issues to make a Data Mining process successfully. However, it is difficult for human experts to evaluate many thousands of rules from a large Dataset with noises completely. To reduce the costs of rule evaluation procedures, we have developed the rule evaluation support method with rule evaluation models, which are obtained with objective indices of mined classification rules and evaluations of a human expert for each rule. To evaluate performances of learning algorithms for constructing rule evaluation models, we have done a case study on the meningitis Data Mining as an actual problem. Then we discuss the availability of our rule evaluation support method.
A Kamel Tari - One of the best experts on this subject based on the ideXlab platform.
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Ontology for knowledge management and improvement of Data Mining Result
ICSDM 2011 - Proceedings 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services, 2011Co-Authors: Hayette Khaled, Tahar Kechadi, A Kamel TariAbstract:Nowadays, large bodies of Data in different domains are collected and stored. An efficient extraction of useful knowledge from these Data becomes a huge challenge. This leads to the need for developing distributed Data Mining techniques (DDM). Moreover, it creates a complex problem of the management of the mined Results. To solve this problem, we propose the Knowledge Map Ontology (KMO) architecture that allows an efficient representation of knowledge to guide the users in the extraction of such knowledge. KMO uses repositories built from Ontologies. The distribution of this architecture is done according to Tree P2P (TreeP) because Ontologies are structured as trees. We show that this architecture is very efficient and necessary in the field, where knowledge is distributed, varied, and representing very large quantities of Data.
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ICSDM - Ontology for knowledge management and improvement of Data Mining Result
Proceedings 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services, 2011Co-Authors: Hayette Khaled, Tahar Kechadi, A Kamel TariAbstract:Nowadays, large bodies of Data in different domains are collected and stored. An efficient extraction of useful knowledge from these Data becomes a huge challenge. This leads to the need for developing distributed Data Mining techniques (DDM). Moreover, it creates a complex problem of the management of the mined Results. To solve this problem, we propose the Knowledge Map Ontology (KMO) architecture that allows an efficient representation of knowledge to guide the users in the extraction of such knowledge. KMO uses repositories built from Ontologies. The distribution of this architecture is done according to Tree P2P (TreeP) because Ontologies are structured as trees. We show that this architecture is very efficient and necessary in the field, where knowledge is distributed, varied, and representing very large quantities of Data.
Hidenao Abe - One of the best experts on this subject based on the ideXlab platform.
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PKAW - Evaluating learning algorithms with meta-learning schemes for a rule evaluation support method based on objective indices
Advances in Knowledge Acquisition and Management, 2006Co-Authors: Hidenao Abe, Shusaku Tsumoto, Miho Ohsaki, Hideto Yokoi, Takahira YamaguchiAbstract:In this paper, we present evaluations of learning algorithms for a novel rule evaluation support method in Data Mining post-processing, which is one of the key processes in a Data Mining process. It is difficult for human experts to evaluate many thousands of rules from a large Dataset with noises completely. To reduce the costs of rule evaluation task, we have developed the rule evaluation support method with rule evaluation models, which are learned from a Dataset consisted of objective indices and evaluations of a human expert for each rule. To enhance adaptability of rule evaluation models, we introduced a constructive meta-learning system to choose proper learning algorithms for constructing them. Then, we have done a case study on the meningitis Data Mining Result, the hepatitis Data Mining Results and rule sets from the eight UCI Datasets.
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evaluating a rule evaluation support method with learning models based on objective rule evaluation indices a case study with a meningitis Data Mining Result
International Conference Hybrid Intelligent Systems, 2005Co-Authors: Hidenao Abe, Shusaku Tsumoto, Miho Ohsaki, Takahira YamaguchiAbstract:In this paper, we present a novel rule evaluation support method for post-processing of mined Results with rule evaluation models based on objective indices. Post-processing of mined Results is one of the key issues to make a Data Mining process successfully. However, it is difficult for human experts to evaluate many thousands of rules from a large Dataset with noises completely. To reduce the costs of rule evaluation procedures, we have developed the rule evaluation support method with rule evaluation models, which are obtained with objective indices of mined classification rules and evaluations of a human expert for each rule. To evaluate performances of learning algorithms for constructing rule evaluation models, we have done a case study on the meningitis Data Mining as an actual problem. Then we discuss the availability of our rule evaluation support method.
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HIS - Evaluating a rule evaluation support method with learning models based on objective rule evaluation indices - a case study with a meningitis Data Mining Result
Fifth International Conference on Hybrid Intelligent Systems (HIS'05), 2005Co-Authors: Hidenao Abe, Shusaku Tsumoto, Miho Ohsaki, Takahira YamaguchiAbstract:In this paper, we present a novel rule evaluation support method for post-processing of mined Results with rule evaluation models based on objective indices. Post-processing of mined Results is one of the key issues to make a Data Mining process successfully. However, it is difficult for human experts to evaluate many thousands of rules from a large Dataset with noises completely. To reduce the costs of rule evaluation procedures, we have developed the rule evaluation support method with rule evaluation models, which are obtained with objective indices of mined classification rules and evaluations of a human expert for each rule. To evaluate performances of learning algorithms for constructing rule evaluation models, we have done a case study on the meningitis Data Mining as an actual problem. Then we discuss the availability of our rule evaluation support method.
Hayette Khaled - One of the best experts on this subject based on the ideXlab platform.
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Ontology for knowledge management and improvement of Data Mining Result
ICSDM 2011 - Proceedings 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services, 2011Co-Authors: Hayette Khaled, Tahar Kechadi, A Kamel TariAbstract:Nowadays, large bodies of Data in different domains are collected and stored. An efficient extraction of useful knowledge from these Data becomes a huge challenge. This leads to the need for developing distributed Data Mining techniques (DDM). Moreover, it creates a complex problem of the management of the mined Results. To solve this problem, we propose the Knowledge Map Ontology (KMO) architecture that allows an efficient representation of knowledge to guide the users in the extraction of such knowledge. KMO uses repositories built from Ontologies. The distribution of this architecture is done according to Tree P2P (TreeP) because Ontologies are structured as trees. We show that this architecture is very efficient and necessary in the field, where knowledge is distributed, varied, and representing very large quantities of Data.
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ICSDM - Ontology for knowledge management and improvement of Data Mining Result
Proceedings 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services, 2011Co-Authors: Hayette Khaled, Tahar Kechadi, A Kamel TariAbstract:Nowadays, large bodies of Data in different domains are collected and stored. An efficient extraction of useful knowledge from these Data becomes a huge challenge. This leads to the need for developing distributed Data Mining techniques (DDM). Moreover, it creates a complex problem of the management of the mined Results. To solve this problem, we propose the Knowledge Map Ontology (KMO) architecture that allows an efficient representation of knowledge to guide the users in the extraction of such knowledge. KMO uses repositories built from Ontologies. The distribution of this architecture is done according to Tree P2P (TreeP) because Ontologies are structured as trees. We show that this architecture is very efficient and necessary in the field, where knowledge is distributed, varied, and representing very large quantities of Data.
Miho Ohsaki - One of the best experts on this subject based on the ideXlab platform.
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PKAW - Evaluating learning algorithms with meta-learning schemes for a rule evaluation support method based on objective indices
Advances in Knowledge Acquisition and Management, 2006Co-Authors: Hidenao Abe, Shusaku Tsumoto, Miho Ohsaki, Hideto Yokoi, Takahira YamaguchiAbstract:In this paper, we present evaluations of learning algorithms for a novel rule evaluation support method in Data Mining post-processing, which is one of the key processes in a Data Mining process. It is difficult for human experts to evaluate many thousands of rules from a large Dataset with noises completely. To reduce the costs of rule evaluation task, we have developed the rule evaluation support method with rule evaluation models, which are learned from a Dataset consisted of objective indices and evaluations of a human expert for each rule. To enhance adaptability of rule evaluation models, we introduced a constructive meta-learning system to choose proper learning algorithms for constructing them. Then, we have done a case study on the meningitis Data Mining Result, the hepatitis Data Mining Results and rule sets from the eight UCI Datasets.
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evaluating a rule evaluation support method with learning models based on objective rule evaluation indices a case study with a meningitis Data Mining Result
International Conference Hybrid Intelligent Systems, 2005Co-Authors: Hidenao Abe, Shusaku Tsumoto, Miho Ohsaki, Takahira YamaguchiAbstract:In this paper, we present a novel rule evaluation support method for post-processing of mined Results with rule evaluation models based on objective indices. Post-processing of mined Results is one of the key issues to make a Data Mining process successfully. However, it is difficult for human experts to evaluate many thousands of rules from a large Dataset with noises completely. To reduce the costs of rule evaluation procedures, we have developed the rule evaluation support method with rule evaluation models, which are obtained with objective indices of mined classification rules and evaluations of a human expert for each rule. To evaluate performances of learning algorithms for constructing rule evaluation models, we have done a case study on the meningitis Data Mining as an actual problem. Then we discuss the availability of our rule evaluation support method.
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HIS - Evaluating a rule evaluation support method with learning models based on objective rule evaluation indices - a case study with a meningitis Data Mining Result
Fifth International Conference on Hybrid Intelligent Systems (HIS'05), 2005Co-Authors: Hidenao Abe, Shusaku Tsumoto, Miho Ohsaki, Takahira YamaguchiAbstract:In this paper, we present a novel rule evaluation support method for post-processing of mined Results with rule evaluation models based on objective indices. Post-processing of mined Results is one of the key issues to make a Data Mining process successfully. However, it is difficult for human experts to evaluate many thousands of rules from a large Dataset with noises completely. To reduce the costs of rule evaluation procedures, we have developed the rule evaluation support method with rule evaluation models, which are obtained with objective indices of mined classification rules and evaluations of a human expert for each rule. To evaluate performances of learning algorithms for constructing rule evaluation models, we have done a case study on the meningitis Data Mining as an actual problem. Then we discuss the availability of our rule evaluation support method.
