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    Comparative Analysis of Deterministic and Nondeterministic Decision Trees

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    Type
    Book
    Authors
    Moshkov, Mikhail cc
    KAUST Department
    Applied Mathematics and Computational Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Extensions of Dynamic Programming, Machine Learning and Discrete Optimization Research Group
    Date
    2020-03-13
    Online Publication Date
    2020-03-13
    Print Publication Date
    2020
    Permanent link to this record
    http://hdl.handle.net/10754/662710
    
    Metadata
    Show full item record
    Abstract
    This book compares four parameters of problems in arbitrary information systems: complexity of problem representation and complexity of deterministic, nondeterministic, and strongly nondeterministic decision trees for problem solving. Deterministic decision trees are widely used as classifiers, as a means of knowledge representation, and as algorithms. Nondeterministic (strongly nondeterministic) decision trees can be interpreted as systems of true decision rules that cover all objects (objects from one decision class). This book develops tools for the study of decision trees, including bounds on complexity and algorithms for construction of decision trees for decision tables with many-valued decisions. It considers two approaches to the investigation of decision trees for problems in information systems: local, when decision trees can use only attributes from the problem representation; and global, when decision trees can use arbitrary attributes from the information system. For both approaches, it describes all possible types of relationships among the four parameters considered and discusses the algorithmic problems related to decision tree optimization. The results presented are useful for researchers who apply decision trees and rules to algorithm design and to data analysis, especially those working in rough set theory, test theory and logical analysis of data. This book can also be used as the basis for graduate courses.
    Citation
    Moshkov, M. (2020). Comparative Analysis of Deterministic and Nondeterministic Decision Trees. Intelligent Systems Reference Library. doi:10.1007/978-3-030-41728-4
    Publisher
    Springer Nature
    ISBN
    9783030417277
    9783030417284
    DOI
    10.1007/978-3-030-41728-4
    Additional Links
    http://link.springer.com/10.1007/978-3-030-41728-4
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-030-41728-4
    Scopus Count
    Collections
    Applied Mathematics and Computational Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division; Books

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