Rough Sets and Boolean Reasoning

Rough Sets and Boolean Reasoning

Hung Son Nguyen (Warsaw University, Poland)
Copyright: © 2008 |Pages: 32
DOI: 10.4018/978-1-59904-552-8.ch002
OnDemand PDF Download:
$37.50

Abstract

This chapter presents the Boolean reasoning approach to problem solving and its applications in Rough sets. The Boolean reasoning approach has become a powerful tool for designing effective and accurate solutions for many problems in decision-making, approximate reasoning and optimization. In recent years, Boolean reasoning has become a recognized technique for developing many interesting concept approximation methods in rough set theory. This chapter presents a general framework for concept approximation by combining the classical Boolean reasoning method with many modern techniques in machine learning and data mining. This modified approach - called “the approximate Boolean reasoning” methodology - has been proposed as an even more powerful tool for problem solving in rough set theory and its applications in data mining. Through some most representative applications in many KDD problems including feature selection, feature extraction, data preprocessing, classification of decision rules and decision trees, association analysis, the author hopes to convince that the proposed approach not only maintains all the merits of its antecedent but also owns the possibility of balancing between quality of the designed solution and its computational time.

Complete Chapter List

Search this Book:
Reset
Table of Contents
Acknowledgment
Chapter 1
Piotr Wasilewski, Dominik Slezak
We present three types of knowledge, which can be specified according to the Rough Set theory. Then, we present three corresponding types of... Sample PDF
Foundations of Rough Sets from Vagueness Perspective
$37.50
Chapter 2
Hung Son Nguyen
This chapter presents the Boolean reasoning approach to problem solving and its applications in Rough sets. The Boolean reasoning approach has... Sample PDF
Rough Sets and Boolean Reasoning
$37.50
Chapter 3
Richard Jensen
Feature selection aims to determine a minimal feature subset from a problem domain while retaining a suitably high accuracy in representing the... Sample PDF
Rough Set-Based Feature Selection: A Review
$37.50
Chapter 4
Yiyu Yao
Rough set analysis (RSA) and formal concept analysis (FCA) are two theories of intelligent data analysis. They can be compared, combined and applied... Sample PDF
Rough Set Analysis and Formal Concept Analysis
$37.50
Chapter 5
Theresa Beaubouef, Frederick E Petry
This chapter discusses ways in which rough set theory can enhance databases by allowing for the management of uncertainty. Rough sets can be... Sample PDF
Rough Sets: A Versatile Theory for Approaches to Uncertainty Management in Databases
$37.50
Chapter 6
Cory J. Butz
In this chapter, we review a graphical framework for reasoning from data, called rough set flow graphs (RSFGs), and point out issues of current... Sample PDF
Current Trends in Rough Set Flow Graphs
$37.50
Chapter 7
Annibal Parracho Sant’Anna
A new index of quality of approximation, called the index of mutual information, is proposed in this chapter. It measures the mutual information... Sample PDF
Probabilistic Indices of Quality of Approximation
$37.50
Chapter 8
Zbigniew W. Ras, Elzbieta M. Wyrzykowska
Action rules can be seen as logical terms describing knowledge about possible actions associated with objects which is hidden in a decision system.... Sample PDF
Extended Action Rule Discovery Based on Single Classification Rules and Reducts
$37.50
Chapter 9
James F Peters
This paper introduces a monocular vision system that learns with approximation spaces to control the pan and tilt operations of a digital camera... Sample PDF
Monocular Vision System that Learns with Approximation Spaces
$37.50
Chapter 10
Tomasz G. Smolinski, Astrid A. Prinz
Classification of sampled continuous signals into one of a finite number of predefined classes is possible when some distance measure between the... Sample PDF
Hybridization of Rough Setsand Multi-ObjectiveEvolutionary Algorithms forClassificatory SignalDecomposition
$37.50
Chapter 11
Jerzy W. Grzymala-Busse, Zdzislaw S. Hippe, Teresa Mroczek
Results of our research on using two approaches, both based on rough sets, to mining three data sets describing bed caking during the hop extraction... Sample PDF
Two Rough Set Approaches to Mining Hop Extraction Data
$37.50
Chapter 12
Krzysztof Pancerz, Zbigniew Suraj
This chapter constitutes the continuation of a new research trend binding rough set theory with concurrency theory. In general, this trend concerns... Sample PDF
Rough Sets for Discovering Concurrent System Models from Data Tables
$37.50
About the Contributors