Ebook: Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory

Intelligent decision support is based on human knowledge related to a specific part of a real or abstract world. When the knowledge is gained by experience, it is induced from empirical data. The data structure, called an information system, is a record of objects described by a set of attributes.
Knowledge is understood here as an ability to classify objects. Objects being in the same class are indiscernible by means of attributes and form elementary building blocks (granules, atoms). In particular, the granularity of knowledge causes that some notions cannot be expressed precisely within available knowledge and can be defined only vaguely. In the rough sets theory created by Z. Pawlak each imprecise concept is replaced by a pair of precise concepts called its lower and upper approximation. These approximations are fundamental tools and reasoning about knowledge.
The rough sets philosophy turned out to be a very effective, new tool with many successful real-life applications to its credit.
It is worthwhile stressing that no auxiliary assumptions are needed about data, like probability or membership function values, which is its great advantage.
The present book reveals a wide spectrum of applications of the rough set concept, giving the reader the flavor of, and insight into, the methodology of the newly developed disciplines. Although the book emphasizes applications, comparison with other related methods and further developments receive due attention.

---

Intelligent decision support is based on human knowledge related to a specific part of a real or abstract world. When the knowledge is gained by experience, it is induced from empirical data. The data structure, called an information system, is a record of objects described by a set of attributes.
Knowledge is understood here as an ability to classify objects. Objects being in the same class are indiscernible by means of attributes and form elementary building blocks (granules, atoms). In particular, the granularity of knowledge causes that some notions cannot be expressed precisely within available knowledge and can be defined only vaguely. In the rough sets theory created by Z. Pawlak each imprecise concept is replaced by a pair of precise concepts called its lower and upper approximation. These approximations are fundamental tools and reasoning about knowledge.
The rough sets philosophy turned out to be a very effective, new tool with many successful real-life applications to its credit.
It is worthwhile stressing that no auxiliary assumptions are needed about data, like probability or membership function values, which is its great advantage.
The present book reveals a wide spectrum of applications of the rough set concept, giving the reader the flavor of, and insight into, the methodology of the newly developed disciplines. Although the book emphasizes applications, comparison with other related methods and further developments receive due attention.

---

Intelligent decision support is based on human knowledge related to a specific part of a real or abstract world. When the knowledge is gained by experience, it is induced from empirical data. The data structure, called an information system, is a record of objects described by a set of attributes.
Knowledge is understood here as an ability to classify objects. Objects being in the same class are indiscernible by means of attributes and form elementary building blocks (granules, atoms). In particular, the granularity of knowledge causes that some notions cannot be expressed precisely within available knowledge and can be defined only vaguely. In the rough sets theory created by Z. Pawlak each imprecise concept is replaced by a pair of precise concepts called its lower and upper approximation. These approximations are fundamental tools and reasoning about knowledge.
The rough sets philosophy turned out to be a very effective, new tool with many successful real-life applications to its credit.
It is worthwhile stressing that no auxiliary assumptions are needed about data, like probability or membership function values, which is its great advantage.
The present book reveals a wide spectrum of applications of the rough set concept, giving the reader the flavor of, and insight into, the methodology of the newly developed disciplines. Although the book emphasizes applications, comparison with other related methods and further developments receive due attention.

Content:
Front Matter....Pages i-xvii
Front Matter....Pages 1-1
LERS-A System for Learning from Examples Based on Rough Sets....Pages 3-18
Rough Sets in Computer Implementation of Rule-Based Control of Industrial Processes....Pages 19-31
Analysis of Diagnostic Symptoms in Vibroacoustic Diagnostics by Means of the Rough Sets Theory....Pages 33-48
Knowledge-Based Process Control Using Rough Sets....Pages 49-60
Acquisition of Control Algorithms from Operation Data....Pages 61-75
Rough Classification of HSV Patients....Pages 77-93
Surgical Wound Infection — Conducive Factors and Their Mutual Dependencies....Pages 95-110
Fuzzy Inference System Based on Rough Sets and Its Application to Medical Diagnosis....Pages 111-117
Analysis of Structure — Activity Relationships of Quaternary Ammonium Compounds....Pages 119-136
Rough Sets-Based Study of Voter Preference in 1988 U.S.A. Presidential Election....Pages 137-151
An Application of Rough Set Theory in the Control of Water Conditions on a Polder....Pages 153-163
Use of “Rough Sets” Method to Draw Premonitory Factors for Earthquakes by Emphasing Gas Geochemistry: The Case of a Low Seismic Activity Context, in Belgium....Pages 165-179
Rough Sets and Some Aspects of Logic Synthesis....Pages 181-199
Front Matter....Pages 201-201
Putting Rough Sets and Fuzzy Sets Together....Pages 203-232
Applications of Fuzzy-Rough Classifications to Logics....Pages 233-250
Comparison of the Rough Sets Approach and Probabilistic Data Analysis Techniques on a Common Set of Medical Data....Pages 251-265
Some Experiments to Compare Rough Sets Theory and Ordinal Statistical Methods....Pages 267-286
Topological and Fuzzy Rough Sets....Pages 287-304
On Convergence of Rough Sets....Pages 305-311
Front Matter....Pages 313-313
Maintenance of Knowledge in Dynamic Information Systems....Pages 315-329
Front Matter....Pages 313-313
The Discernibility Matrices and Functions in Information Systems....Pages 331-362
Sensitivity of Rough Classification to Changes in Norms of Attributes....Pages 363-372
Discretization of Condition Attributes Space....Pages 373-389
Consequence Relations and Information Systems....Pages 391-399
Rough Grammar for High Performance Management of Processes on a Distributed System....Pages 401-418
Learning Classification Rules from Database in the Context of Knowledge-Acquisition and -Representation....Pages 419-444
‘Roughdas’ and ‘Roughclass’ Software Implementations of the Rough Sets Approach....Pages 445-456
Back Matter....Pages 457-473

---

Intelligent decision support is based on human knowledge related to a specific part of a real or abstract world. When the knowledge is gained by experience, it is induced from empirical data. The data structure, called an information system, is a record of objects described by a set of attributes.
Knowledge is understood here as an ability to classify objects. Objects being in the same class are indiscernible by means of attributes and form elementary building blocks (granules, atoms). In particular, the granularity of knowledge causes that some notions cannot be expressed precisely within available knowledge and can be defined only vaguely. In the rough sets theory created by Z. Pawlak each imprecise concept is replaced by a pair of precise concepts called its lower and upper approximation. These approximations are fundamental tools and reasoning about knowledge.
The rough sets philosophy turned out to be a very effective, new tool with many successful real-life applications to its credit.
It is worthwhile stressing that no auxiliary assumptions are needed about data, like probability or membership function values, which is its great advantage.
The present book reveals a wide spectrum of applications of the rough set concept, giving the reader the flavor of, and insight into, the methodology of the newly developed disciplines. Although the book emphasizes applications, comparison with other related methods and further developments receive due attention.

Content:
Front Matter....Pages i-xvii
Front Matter....Pages 1-1
LERS-A System for Learning from Examples Based on Rough Sets....Pages 3-18
Rough Sets in Computer Implementation of Rule-Based Control of Industrial Processes....Pages 19-31
Analysis of Diagnostic Symptoms in Vibroacoustic Diagnostics by Means of the Rough Sets Theory....Pages 33-48
Knowledge-Based Process Control Using Rough Sets....Pages 49-60
Acquisition of Control Algorithms from Operation Data....Pages 61-75
Rough Classification of HSV Patients....Pages 77-93
Surgical Wound Infection — Conducive Factors and Their Mutual Dependencies....Pages 95-110
Fuzzy Inference System Based on Rough Sets and Its Application to Medical Diagnosis....Pages 111-117
Analysis of Structure — Activity Relationships of Quaternary Ammonium Compounds....Pages 119-136
Rough Sets-Based Study of Voter Preference in 1988 U.S.A. Presidential Election....Pages 137-151
An Application of Rough Set Theory in the Control of Water Conditions on a Polder....Pages 153-163
Use of “Rough Sets” Method to Draw Premonitory Factors for Earthquakes by Emphasing Gas Geochemistry: The Case of a Low Seismic Activity Context, in Belgium....Pages 165-179
Rough Sets and Some Aspects of Logic Synthesis....Pages 181-199
Front Matter....Pages 201-201
Putting Rough Sets and Fuzzy Sets Together....Pages 203-232
Applications of Fuzzy-Rough Classifications to Logics....Pages 233-250
Comparison of the Rough Sets Approach and Probabilistic Data Analysis Techniques on a Common Set of Medical Data....Pages 251-265
Some Experiments to Compare Rough Sets Theory and Ordinal Statistical Methods....Pages 267-286
Topological and Fuzzy Rough Sets....Pages 287-304
On Convergence of Rough Sets....Pages 305-311
Front Matter....Pages 313-313
Maintenance of Knowledge in Dynamic Information Systems....Pages 315-329
Front Matter....Pages 313-313
The Discernibility Matrices and Functions in Information Systems....Pages 331-362
Sensitivity of Rough Classification to Changes in Norms of Attributes....Pages 363-372
Discretization of Condition Attributes Space....Pages 373-389
Consequence Relations and Information Systems....Pages 391-399
Rough Grammar for High Performance Management of Processes on a Distributed System....Pages 401-418
Learning Classification Rules from Database in the Context of Knowledge-Acquisition and -Representation....Pages 419-444
‘Roughdas’ and ‘Roughclass’ Software Implementations of the Rough Sets Approach....Pages 445-456
Back Matter....Pages 457-473
....