Decision Trees Versus Systems Of Decision Rules
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Decision Trees Versus Systems of Decision Rules
Author: Kerven Durdymyradov
language: en
Publisher: Springer Nature
Release Date: 2024-12-23
This book explores, within the framework of rough set theory, the complexity of decision trees and decision rule systems and the relationships between them for problems over information systems, for decision tables from closed classes, and for problems involving formal languages. Decision trees and systems of decision rules are widely used as means of representing knowledge, as classifiers that predict decisions for new objects, as well as algorithms for solving various problems of fault diagnosis, combinatorial optimization, etc. Decision trees and systems of decision rules are among the most interpretable models of knowledge representation and classification. Investigating the relationships between these two models is an important task in computer science. The possibilities of transforming decision rule systems into decision trees are being studied in detail. The results are useful for researchers using decision trees and decision rule systems in data analysis, especially in rough set theory, logical analysis of data, and test theory. This book is also used to create courses for graduate students.
Recent Challenges in Intelligent Information and Database Systems
This book constitutes the proceedings of the 16th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2024, held in Ras Al Khaimah, UAE, during April 15–18, 2024. The 58 full papers are presented in this book were carefully reviewed and selected from 251 submissions. They are organized in topical sections as follows: Part One: AI-driven Cybersecurity Solutions; AI-driven Medical Analytics; Computational Intelligence; and Data Modelling and Information Systems. Part Two: Image and Video Processing; Prediction and Recommendation Systems; and Text, Speech and Natural Language Processing.
Decision and Inhibitory Trees and Rules for Decision Tables with Many-valued Decisions
The results presented here (including the assessment of a new tool – inhibitory trees) offer valuable tools for researchers in the areas of data mining, knowledge discovery, and machine learning, especially those whose work involves decision tables with many-valued decisions. The authors consider various examples of problems and corresponding decision tables with many-valued decisions, discuss the difference between decision and inhibitory trees and rules, and develop tools for their analysis and design. Applications include the study of totally optimal (optimal in relation to a number of criteria simultaneously) decision and inhibitory trees and rules; the comparison of greedy heuristics for tree and rule construction as single-criterion and bi-criteria optimization algorithms; and the development of a restricted multi-pruning approach used in classification and knowledge representation.