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Decision and Inhibitory Trees and Rules for Decision Tables with Many-valued Decisions

2019-03-13 Technology & Engineering
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Release : 2019-03-13
Genre : Technology & Engineering
Kind : eBook
Book Rating : 547/5 ( reviews)

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Book Synopsis Decision and Inhibitory Trees and Rules for Decision Tables with Many-valued Decisions by : Fawaz Alsolami

Download or read book Decision and Inhibitory Trees and Rules for Decision Tables with Many-valued Decisions written by Fawaz Alsolami. This book was released on 2019-03-13. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Intelligence Science III

2021-04-14 Computers
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Release : 2021-04-14
Genre : Computers
Kind : eBook
Book Rating : 26X/5 ( reviews)

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Book Synopsis Intelligence Science III by : Zhongzhi Shi

Download or read book Intelligence Science III written by Zhongzhi Shi. This book was released on 2021-04-14. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed post-conference proceedings of the 4th International Conference on Intelligence Science, ICIS 2020, held in Durgapur, India, in February 2021 (originally November 2020). The 23 full papers and 4 short papers presented were carefully reviewed and selected from 42 submissions. One extended abstract is also included. They deal with key issues in brain cognition; uncertain theory; machine learning; data intelligence; language cognition; vision cognition; perceptual intelligence; intelligent robot; and medical artificial intelligence.

Transactions on Rough Sets XXII

2020-12-16 Computers
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Release : 2020-12-16
Genre : Computers
Kind : eBook
Book Rating : 981/5 ( reviews)

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Book Synopsis Transactions on Rough Sets XXII by : James F. Peters

Download or read book Transactions on Rough Sets XXII written by James F. Peters. This book was released on 2020-12-16. Available in PDF, EPUB and Kindle. Book excerpt: The LNCS journal Transactions on Rough Sets is devoted to the entire spectrum of rough sets related issues, from logical and mathematical foundations, through all aspects of rough set theory and its applications, such as data mining, knowledge discovery, and intelligent information processing, to relations between rough sets and other approaches to uncertainty, vagueness, and incompleteness, such as fuzzy sets and theory of evidence. Volume XXII in the series is a continuation of a number of research streams that have grown out of the seminal work of Zdzislaw Pawlak during the first decade of the 21st century.

Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining

2018-05-22 Technology & Engineering
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Release : 2018-05-22
Genre : Technology & Engineering
Kind : eBook
Book Rating : 397/5 ( reviews)

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Book Synopsis Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining by : Hassan AbouEisha

Download or read book Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining written by Hassan AbouEisha. This book was released on 2018-05-22. Available in PDF, EPUB and Kindle. Book excerpt: Dynamic programming is an efficient technique for solving optimization problems. It is based on breaking the initial problem down into simpler ones and solving these sub-problems, beginning with the simplest ones. A conventional dynamic programming algorithm returns an optimal object from a given set of objects. This book develops extensions of dynamic programming, enabling us to (i) describe the set of objects under consideration; (ii) perform a multi-stage optimization of objects relative to different criteria; (iii) count the number of optimal objects; (iv) find the set of Pareto optimal points for bi-criteria optimization problems; and (v) to study relationships between two criteria. It considers various applications, including optimization of decision trees and decision rule systems as algorithms for problem solving, as ways for knowledge representation, and as classifiers; optimization of element partition trees for rectangular meshes, which are used in finite element methods for solving PDEs; and multi-stage optimization for such classic combinatorial optimization problems as matrix chain multiplication, binary search trees, global sequence alignment, and shortest paths. The results presented are useful for researchers in combinatorial optimization, data mining, knowledge discovery, machine learning, and finite element methods, especially those working in rough set theory, test theory, logical analysis of data, and PDE solvers. This book can be used as the basis for graduate courses.

Comparative Analysis of Deterministic and Nondeterministic Decision Trees

2020-03-14 Technology & Engineering
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Release : 2020-03-14
Genre : Technology & Engineering
Kind : eBook
Book Rating : 28X/5 ( reviews)

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Book Synopsis Comparative Analysis of Deterministic and Nondeterministic Decision Trees by : Mikhail Moshkov

Download or read book Comparative Analysis of Deterministic and Nondeterministic Decision Trees written by Mikhail Moshkov. This book was released on 2020-03-14. Available in PDF, EPUB and Kindle. Book excerpt: 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.

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