Titlebook: Decision Trees with Hypotheses; Mohammad Azad,Igor Chikalov,Beata Zielosko Book 2022 The Editor(s) (if applicable) and The Author(s), unde

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Dynamic Programming Algorithms for?Minimization of?Decision Tree Complexitycomputer experiments on various data sets from the UCI ML Repository and randomly generated Boolean functions. Decision trees with hypotheses, generally, have less complexity than conventional decision trees, i.e., they are more understandable and more suitable as a means for knowledge representatio

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Construction of?Optimal Decision Trees and?Deriving Decision Rules from?Themumber of working (nonterminal) nodes. We make computer experiments on various data sets from the UCI Machine Learning Repository and randomly generated Boolean functions to compare the length and coverage of decision rules derived from different kinds of optimal decision trees. The obtained results

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Decision Trees with?Hypotheses for?Recognition of?Monotone Boolean Functions and?for?Sortingt elements from linearly ordered set, .. For each of these problems, we compare the complexity of five types of optimal (relative to the depth or the number of realizable nodes) decision trees. We also study the complexity of decision trees constructed by greedy algorithms and analyze the length of

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Infinite Binary Information Systems. Decision Trees of?Types 1, 2, and?3 which consists of an infinite set of elements and an infinite set of two-valued functions (attributes) defined on the set of elements. We consider the notion of a problem over information system, which is described by a finite number of attributes: for a given element, we should recognize values of

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Construction of?Optimal Decision Trees and?Deriving Decision Rules from?Themd Boolean functions to compare the length and coverage of decision rules derived from different kinds of optimal decision trees. The obtained results show that the decision rules derived from optimal decision trees with hypotheses in many cases are better than the rules derived from optimal conventional decision trees.

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