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Synopsis
Model selection is one of the fundamental tasks of scientific inquiry. The most widely used methods such as ROC analysis do not take sampling uncertainty into account. To improve the robustness of model selection, the author developed a model selection method capable to incorporate sampling uncertainty. She captured the sampling uncertainty by using the bootstrap technique, and quantified the sampling uncertainty by introducing fuzzy numbers. In the book, the author applied the model selection system to a variety of real-world databases with respect to binary classifications. Among the tested datasets, the method performs in line with the traditional ROC analysis, whereas it provides the fuzzy presentation of ROC curves based on which not only the predictive accuracy but also the degree of sampling uncertainty can be addressed. In addition, the author developed a computer tool implementing the system, which eases the tedious procedures in model selection.
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About the Author
Bei held BSc in Computer Science and Economics. Thereafter she obtained a MSc in Economics & Informatics from Erasmus University, for which she produced a thesis on model selection under sampling uncertainty using fuzzy numbers. Now she is a PhD candidate at TU Delft, working for RI and KWR. Her field of interest is computing techniques.
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Model Selection Under Sampling Uncertainty Using Fuzzy Numbers
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LAP LAMBERT Academic Publishing Feb 2011, 2011
ISBN 10: 3843394644
ISBN 13: 9783843394642
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Model selection is one of the fundamental tasks of scientific inquiry. The most widely used methods such as ROC analysis do not take sampling uncertainty into account. To improve the robustness of model selection, the author developed a model selection method capable to incorporate sampling uncertainty. She captured the sampling uncertainty by using the bootstrap technique, and quantified the sampling uncertainty by introducing fuzzy numbers. In the book, the author applied the model selection system to a variety of real-world databases with respect to binary classifications. Among the tested datasets, the method performs in line with the traditional ROC analysis, whereas it provides the fuzzy presentation of ROC curves based on which not only the predictive accuracy but also the degree of sampling uncertainty can be addressed. In addition, the author developed a computer tool implementing the system, which eases the tedious procedures in model selection. 104 pp. Englisch. Seller Inventory # 9783843394642
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Model Selection Under Sampling Uncertainty Using Fuzzy Numbers
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Wen BeiBei held BSc in Computer Science and Economics. Thereafter she obtained a MSc in Economics & Informatics from Erasmus University, for which she produced a thesis on model selection under sampling uncertainty using fuzzy num. Seller Inventory # 5469271
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Model Selection Under Sampling Uncertainty Using Fuzzy Numbers
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LAP LAMBERT Academic Publishing Feb 2011, 2011
ISBN 10: 3843394644
ISBN 13: 9783843394642
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Model selection is one of the fundamental tasks of scientific inquiry. The most widely used methods such as ROC analysis do not take sampling uncertainty into account. To improve the robustness of model selection, the author developed a model selection method capable to incorporate sampling uncertainty. She captured the sampling uncertainty by using the bootstrap technique, and quantified the sampling uncertainty by introducing fuzzy numbers. In the book, the author applied the model selection system to a variety of real-world databases with respect to binary classifications. Among the tested datasets, the method performs in line with the traditional ROC analysis, whereas it provides the fuzzy presentation of ROC curves based on which not only the predictive accuracy but also the degree of sampling uncertainty can be addressed. In addition, the author developed a computer tool implementing the system, which eases the tedious procedures in model selection.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 104 pp. Englisch. Seller Inventory # 9783843394642
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Model Selection Under Sampling Uncertainty Using Fuzzy Numbers : A hybrid method
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Model selection is one of the fundamental tasks of scientific inquiry. The most widely used methods such as ROC analysis do not take sampling uncertainty into account. To improve the robustness of model selection, the author developed a model selection method capable to incorporate sampling uncertainty. She captured the sampling uncertainty by using the bootstrap technique, and quantified the sampling uncertainty by introducing fuzzy numbers. In the book, the author applied the model selection system to a variety of real-world databases with respect to binary classifications. Among the tested datasets, the method performs in line with the traditional ROC analysis, whereas it provides the fuzzy presentation of ROC curves based on which not only the predictive accuracy but also the degree of sampling uncertainty can be addressed. In addition, the author developed a computer tool implementing the system, which eases the tedious procedures in model selection. Seller Inventory # 9783843394642
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Model Selection Under Sampling Uncertainty Using Fuzzy Numbers | A hybrid method
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Taschenbuch. Condition: Neu. Model Selection Under Sampling Uncertainty Using Fuzzy Numbers | A hybrid method | Bei Wen | Taschenbuch | 104 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783843394642 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. Seller Inventory # 107085462
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Model Selection Under Sampling Uncertainty Using Fuzzy Numbers: A hybrid method
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