Decision and inhibitory trees and rules for decision tables with many-valued decisions. ([2020])

• Record Type: Book
• Further Information: Note: Fawaz Alsolami, Mohammad Azad, Igor Chikalov, Mikhail Moshkov.
• Authors: Alsolami, Fawaz; Azad, Mohammad; Chikalov, Igor; Moshkov, Mikhail
• Contents: Intro; Preface; Acknowledgements; Contents; 1 Introduction; 1.1 Decision and Inhibitory Interpretations of Decision Tables with Many-valued Decisions; 1.1.1 Decision Interpretation; 1.1.2 Inhibitory Interpretation; 1.2 Main Directions of Study; 1.2.1 Explaining Examples and Preliminary Results; 1.2.2 Extensions of Dynamic Programming for Decision and Inhibitory Trees, Rules, and Systems of Rules; 1.2.3 Study of Decision and Inhibitory Trees and Rule Systems Over Arbitrary Information Systems; 1.3 Contents of Book; 1.3.1 Part I. Explaining Examples and Preliminary Results 1.3.2 Part II. Extensions of Dynamic Programming for Decision and Inhibitory Trees1.3.3 Part III. Extensions of Dynamic Programming for Decision and Inhibitory Rules and Systems of Rules; 1.3.4 Part IV. Study of Decision and Inhibitory Trees and Rule Systems Over Arbitrary Information Systems; 1.4 Use of Book; References; Part I Explaining Examples and Preliminary Results; 2 Explaining Examples; 2.1 Problems and Decision Tables; 2.1.1 Problems with Many-valued Decisions; 2.1.2 Decision Tables Corresponding to Problems; 2.2 Examples of Decision Tables with Many-valued Decisions 2.2.1 Problem of Three Post-offices2.2.2 Traveling Salesman Problem with Four Cities; 2.2.3 Diagnosis of One-Gate Circuit; 2.2.4 Example of Inconsistent Decision Table; 2.3 Difference Between Decision and Inhibitory Rules and Trees; 2.3.1 Prediction Problem; 2.3.2 Knowledge Representation Problem; References; 3 Three Approaches to Handle Inconsistency in Decision Tables; 3.1 Inconsistent Decision Tables and Three Approaches to Handle Them; 3.2 Decision Tables Used in Experiments; 3.3 Comparison of Complexity of Decision Trees; 3.4 Comparison of Accuracy of Classifiers; References 4 Preliminary Results for Decision and Inhibitory Trees, Tests, Rules, and Rule Systems4.1 Main Notions; 4.1.1 Binary Decision Tables with Many-valued Decisions; 4.1.2 Decision Trees, Rule Systems, and Tests; 4.1.3 Inhibitory Trees, Rule Systems, and Tests; 4.1.4 Complementary Decision Table; 4.2 Relationships Among Trees, Rule Systems, and Tests; 4.2.1 Decision Interpretation; 4.2.2 Inhibitory Interpretation; 4.3 Lower Bounds on Complexity of Trees, Rules, Rule Systems, and Tests; 4.3.1 Decision Interpretation; 4.3.2 Inhibitory Interpretation 4.4 Upper Bounds on Complexity of Trees, Rule Systems, and Tests4.4.1 Decision Interpretation; 4.4.2 Inhibitory Interpretation; 4.5 Approximate Algorithms for Optimization of Decision and Inhibitory Tests and Rules; 4.5.1 Greedy Algorithm for Set Cover Problem; 4.5.2 Optimization of Decision Tests; 4.5.3 Optimization of Inhibitory Tests; 4.5.4 Optimization of Decision Rules; 4.5.5 Optimization of Inhibitory Rules; 4.6 Approximate Algorithms for Decision and Inhibitory Tree Optimization; 4.6.1 Optimization of Decision Trees; 4.6.2 Optimization of Inhibitory Trees
• Publisher Details: Cham, Switzerland : Springer
• Publication Date: 2020
• Extent: 1 online resource
• Subjects: 658.4/03; Decision trees; Decision trees; Electronic books
• Languages: English
• ISBNs: 9783030128548; 3030128547
• Related ISBNs: 9783030128531
• Notes: Note: Includes bibliographical references and index.; Note: Description based on online resource; title from digital title page (viewed on May 16, 2019).
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
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