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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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發(fā)表于 2025-3-21 17:55:45 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Decision Trees with Hypotheses
編輯Mohammad Azad,Igor Chikalov,Beata Zielosko
視頻videohttp://file.papertrans.cn/265/264318/264318.mp4
概述Presents the concept of a hypothesis about the values of all attributes.Provides tools for the experimental and theoretical study of decision trees with hypotheses.Compares these decision trees with c
叢書名稱Synthesis Lectures on Intelligent Technologies
圖書封面Titlebook: Decision Trees with Hypotheses;  Mohammad Azad,Igor Chikalov,Beata Zielosko Book 2022 The Editor(s) (if applicable) and The Author(s), unde
描述.In this book, the concept of a hypothesis about the values of all attributes is added to the standard decision tree model, considered, in particular, in test theory and rough set theory. This extension allows us to use the analog of equivalence queries from exact learning and explore decision trees that are based on various combinations of attributes, hypotheses, and proper hypotheses (analog of proper equivalence queries). The two main goals of this book are (i) to provide tools for the experimental and theoretical study of decision trees with hypotheses and (ii) to compare these decision trees with conventional decision trees that use only queries, each based on a single attribute.?.Both experimental and theoretical results show that decision trees with hypotheses can have less complexity than conventional decision trees. These results open up some prospects for using decision trees with hypotheses as a means of knowledge representation and algorithms for computing Boolean functions. The obtained theoretical results and tools for studying decision trees with hypotheses are useful for researchers using decision trees and rules in data analysis. This book can also be used as the b
出版日期Book 2022
關鍵詞Computational Intelligence; Decision Trees; Decision Tree Model; Test Theory; Rough Set Theory
版次1
doihttps://doi.org/10.1007/978-3-031-08585-7
isbn_softcover978-3-031-08587-1
isbn_ebook978-3-031-08585-7Series ISSN 2731-6912 Series E-ISSN 2731-6920
issn_series 2731-6912
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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沙發(fā)
發(fā)表于 2025-3-21 20:30:36 | 只看該作者
Building Better Input RepresentationsIn this chapter, we consider main notions for Part I: decision tables and uncertainty measures, decision trees, and decision rules derived from decision trees.
板凳
發(fā)表于 2025-3-22 03:42:14 | 只看該作者
Introduction,In this chapter, we discuss main goals of the book, two its parts, prospects of using decision trees with hypotheses, and the use of book.
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Mohammad Azad,Igor Chikalov,Beata ZieloskoPresents the concept of a hypothesis about the values of all attributes.Provides tools for the experimental and theoretical study of decision trees with hypotheses.Compares these decision trees with c
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發(fā)表于 2025-3-23 02:02:19 | 只看該作者
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 representation.
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發(fā)表于 2025-3-23 06:17:42 | 只看該作者
Greedy Algorithms for Construction of Decision Trees with Hypotheses the results of computer experiments on various data sets and randomly generated Boolean functions. We also study the length and coverage of decision rules derived from the decision trees constructed by greedy algorithms.
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