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Decision Tree approaches for handling label-uncertainty in classification problems From Possibilistic Similarity Measures to Possibilistic Decision Trees.

Автор:

Ilyes J.

мягкая обложка
164 страниц
2010 год

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After showing the important role that inconsistency could play in assessing possibilistic similarity, a new inconsistency based possibilistic similarity measure, so-called information affinity has been proposed. For each approach, we were faced and solved typical questions for inducing possibilistic decision trees such as how to define an attribute selection measure, how to find the stopping criteria and how leaves should be labeled in such uncertain context. This issue constitutes the second main contribution of this work. Two of the proposed approaches are mainly based on the concept of similarity between possibility distributions. A first main contribution is to propose a so-called possibilistic decision tree which allows to induce decision trees from training data characterized by uncertain class labels where uncertainty is modeled within the quantitative possibility theory framework. This work concerns two important issues in machine learning and reasoning under uncertainty: how to evaluate a similarity relation between two uncertain pieces of information and how to perform classification from uncertain data. Three possibilistic decision tree approaches have been developed.

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Princeton, 1957; Decision Making: An Experimental Approach. Stanford, 1957 (совм. с D. Davidson, S. Siegel); Axiomatic Set Theory. Princeton, I960; Markov Learning Models for Multiperson Interactions. Stanford, 1960 (совм. с R.C.Atkinson); Studies in the Methodology and Foundations of Science: Selected Papers from 1951 to 1969. Dordrecht, 1969; Information and Inference, ed. by J. Hin- tikka and P. Suppes. Dordrecht, 1970; A Probabilistic Theory of Causality. Amst.—Helsinki, 1979; Foundations of Measurement, vol. I: Additive and Polynomial Representations. N. Y, 1971 (совм. с D. H. Krantz, R. D. Luce, A. Tversky); Popper's Analysis of Probability in Quantum Mechanics.— P. A. Schilpp (ed.). The Philosophy of Karl Popper, vol. 2, La Salle, 1974; From Behaviorism to Neobehaviorism.— Theory and Decision, vol. 6, 1975, p. 269-285; Probabilistic Metaphysics. Oxf., 1984; Philosophical Implications of Tarski's Work.— «Journal of Symbolic Logic», 1984, vol. 52, p. 80—91; Foundations of Measurement, vol. II: Geometrical, Threshold and Probabilistic Representations. N.Y., 1989 (совм. с D. H. Krantz, R. D. Luce, A ...»

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Decision Tree approaches for handling label-uncertainty in classification problems From Possibilistic Similarity Measures to Possibilistic Decision Trees. Ilyes J. http://www.booksiti.net.ru/books/22110075

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