The Experts below are selected from a list of 273 Experts worldwide ranked by ideXlab platform
Yanfeng Fan - One of the best experts on this subject based on the ideXlab platform.
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A new approach to Symbolic Classification rule extraction based on SVM
Lecture Notes in Computer Science, 2006Co-Authors: Dexian Zhang, Tiejun Yang, Ziqiang Wang, Yanfeng FanAbstract:There still exist two key problems required to be solved in the Classification rule extraction, i.e. how to select attributes and discretize continuous attributes effectively. The lack of efficient heuristic information is the fundamental reason that affects the performance of currently used approaches. In this paper, a new measure for determining the importance level of the attributes based on the trained SVM is proposed, which is suitable for both continuous attributes and discrete attributes. Based on this new measure, a new approach for rule extraction from trained SVM and Classification problems with continuous attributes is proposed. The performance of the new approach is demonstrated by several computing cases. The experimental results prove that the approach proposed can improve the validity of the extracted rules remarkably compared with other rule extracting approaches, especially for the complicated Classification problems.
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PRICAI - A new approach to Symbolic Classification rule extraction based on SVM
Lecture Notes in Computer Science, 2006Co-Authors: Dexian Zhang, Tiejun Yang, Ziqiang Wang, Yanfeng FanAbstract:There still exist two key problems required to be solved in the Classification rule extraction, i.e. how to select attributes and discretize continuous attributes effectively. The lack of efficient heuristic information is the fundamental reason that affects the performance of currently used approaches. In this paper, a new measure for determining the importance level of the attributes based on the trained SVM is proposed, which is suitable for both continuous attributes and discrete attributes. Based on this new measure, a new approach for rule extraction from trained SVM and Classification problems with continuous attributes is proposed. The performance of the new approach is demonstrated by several computing cases. The experimental results prove that the approach proposed can improve the validity of the extracted rules remarkably compared with other rule extracting approaches, especially for the complicated Classification problems.
Dexian Zhang - One of the best experts on this subject based on the ideXlab platform.
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A new approach to Symbolic Classification rule extraction based on SVM
Lecture Notes in Computer Science, 2006Co-Authors: Dexian Zhang, Tiejun Yang, Ziqiang Wang, Yanfeng FanAbstract:There still exist two key problems required to be solved in the Classification rule extraction, i.e. how to select attributes and discretize continuous attributes effectively. The lack of efficient heuristic information is the fundamental reason that affects the performance of currently used approaches. In this paper, a new measure for determining the importance level of the attributes based on the trained SVM is proposed, which is suitable for both continuous attributes and discrete attributes. Based on this new measure, a new approach for rule extraction from trained SVM and Classification problems with continuous attributes is proposed. The performance of the new approach is demonstrated by several computing cases. The experimental results prove that the approach proposed can improve the validity of the extracted rules remarkably compared with other rule extracting approaches, especially for the complicated Classification problems.
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PRICAI - A new approach to Symbolic Classification rule extraction based on SVM
Lecture Notes in Computer Science, 2006Co-Authors: Dexian Zhang, Tiejun Yang, Ziqiang Wang, Yanfeng FanAbstract:There still exist two key problems required to be solved in the Classification rule extraction, i.e. how to select attributes and discretize continuous attributes effectively. The lack of efficient heuristic information is the fundamental reason that affects the performance of currently used approaches. In this paper, a new measure for determining the importance level of the attributes based on the trained SVM is proposed, which is suitable for both continuous attributes and discrete attributes. Based on this new measure, a new approach for rule extraction from trained SVM and Classification problems with continuous attributes is proposed. The performance of the new approach is demonstrated by several computing cases. The experimental results prove that the approach proposed can improve the validity of the extracted rules remarkably compared with other rule extracting approaches, especially for the complicated Classification problems.
Julia Medori - One of the best experts on this subject based on the ideXlab platform.
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Symbolic Classification methods for patient discharge summaries encoding into icd
International conference natural language processing, 2010Co-Authors: Laurent Kevers, Julia MedoriAbstract:This paper addresses the issue of semi-automatic patient discharge summaries encoding into medical Classifications such as ICD-9- CM. The methods detailed in this paper focus on Symbolic approaches which allow the processing of unannotated corpora without any machine learning. The first method is based on the morphological analysis (MA) of medical terms extracted with hand-crafted linguistic resources. The second one (ELP) relies on the automatic extraction of variants of ICD-9- CM code labels. Each method was evaluated on a set of 19,692 discharge summaries in French from a General Internal Medicine unit. Depending on the number of suggested classes, the MA method resulted in a maximal F-measure of 28.00 and a highest recall of 46.13%. The best F-measure for the second method was 29.43 while the maximal recall was 52.74%. Both methods were then combined. The best recall increased to 60.21% and the maximal F-measure reached 31.64.
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IceTAL - Symbolic Classification methods for patient discharge summaries encoding into ICD
Advances in Natural Language Processing, 2010Co-Authors: Laurent Kevers, Julia MedoriAbstract:This paper addresses the issue of semi-automatic patient discharge summaries encoding into medical Classifications such as ICD-9- CM. The methods detailed in this paper focus on Symbolic approaches which allow the processing of unannotated corpora without any machine learning. The first method is based on the morphological analysis (MA) of medical terms extracted with hand-crafted linguistic resources. The second one (ELP) relies on the automatic extraction of variants of ICD-9- CM code labels. Each method was evaluated on a set of 19,692 discharge summaries in French from a General Internal Medicine unit. Depending on the number of suggested classes, the MA method resulted in a maximal F-measure of 28.00 and a highest recall of 46.13%. The best F-measure for the second method was 29.43 while the maximal recall was 52.74%. Both methods were then combined. The best recall increased to 60.21% and the maximal F-measure reached 31.64.
Harold Hill - One of the best experts on this subject based on the ideXlab platform.
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Evaluation of human gait through observing body movements
2008 International Conference on Intelligent Sensors Sensor Networks and Information Processing, 2008Co-Authors: A. Hesami, Fazel Naghdy, David Stirling, Harold HillAbstract:A new modelling and Classification approach for human gait evaluation is proposed. The body movements are obtained using a sensor suit recording inertial signals that are subsequently modelled on a humanoid frame with 23 degrees of freedom (DOF). Measured signals include position, velocity, acceleration, orientation, angular velocity and angular acceleration. Using the features extracted from the sensory signals, a system with induced Symbolic Classification models, such as decision trees or rule sets, based on a range of several concurrent features has been used to classify deviations from normal gait. It is anticipated that this approach will enable the evaluation of various behaviours including departures from the normal pattern of expected behaviour. The approach is described and the characteristics of the algorithm are presented. The results obtained so far are reported and conclusions are drawn.
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Perception of human gestures through observing body movements
2008 International Conference on Intelligent Sensors Sensor Networks and Information Processing, 2008Co-Authors: A. Hesami, Fazel Naghdy, David Stirling, Harold HillAbstract:A new approach to modelling and Classification of human gait is proposed. Body movements are obtained using a sensor suit that records inertial signals that are subsequently modelled on a humanoid frame with 23 degrees of freedom (DOF). Measured signals include position, velocity, acceleration, orientation, angular velocity and angular acceleration. Using a range of concurrent features extracted from the sensor signals, a system using induced Symbolic Classification models, such as decision trees or rule sets, has been used for Classification of identity. It is anticipated that this approach will also enable the identification of a variety of gestures. The feasibility of generating the identified behaviours in a humanoid robot will be explored. The approach is described and the characteristics of the algorithm are presented. The results obtained so far are reported and conclusions drawn.
Ziqiang Wang - One of the best experts on this subject based on the ideXlab platform.
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A new approach to Symbolic Classification rule extraction based on SVM
Lecture Notes in Computer Science, 2006Co-Authors: Dexian Zhang, Tiejun Yang, Ziqiang Wang, Yanfeng FanAbstract:There still exist two key problems required to be solved in the Classification rule extraction, i.e. how to select attributes and discretize continuous attributes effectively. The lack of efficient heuristic information is the fundamental reason that affects the performance of currently used approaches. In this paper, a new measure for determining the importance level of the attributes based on the trained SVM is proposed, which is suitable for both continuous attributes and discrete attributes. Based on this new measure, a new approach for rule extraction from trained SVM and Classification problems with continuous attributes is proposed. The performance of the new approach is demonstrated by several computing cases. The experimental results prove that the approach proposed can improve the validity of the extracted rules remarkably compared with other rule extracting approaches, especially for the complicated Classification problems.
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PRICAI - A new approach to Symbolic Classification rule extraction based on SVM
Lecture Notes in Computer Science, 2006Co-Authors: Dexian Zhang, Tiejun Yang, Ziqiang Wang, Yanfeng FanAbstract:There still exist two key problems required to be solved in the Classification rule extraction, i.e. how to select attributes and discretize continuous attributes effectively. The lack of efficient heuristic information is the fundamental reason that affects the performance of currently used approaches. In this paper, a new measure for determining the importance level of the attributes based on the trained SVM is proposed, which is suitable for both continuous attributes and discrete attributes. Based on this new measure, a new approach for rule extraction from trained SVM and Classification problems with continuous attributes is proposed. The performance of the new approach is demonstrated by several computing cases. The experimental results prove that the approach proposed can improve the validity of the extracted rules remarkably compared with other rule extracting approaches, especially for the complicated Classification problems.
