Call For Expression Of Interest: Handbook of General Rough Sets

 

 

 

Editor-in -Chief: A Mani

Co-Editor-in-Chief(s): Stefania Boffa, Chris Cornelis, Sheela Ramanna, Beata Zielosko

 

Section Editors: A Mani, Stefania Boffa, Davide Ciucci, Chris Cornelis, Piotr Artemjew, Sheela Ramanna, Yuyi Yao, Beata Zielosko

 

 

 

A handbook of rough sets is planned as a Springer Major Reference Work.

It will consist of 14-15 sections with a total of over 162 chapters (details below). Chapters within the book fall under book sections, while chapter

sections may themselves be substantially long. There are no page restrictions, though contributed chapter sections should be at least three pages long.

 

Researchers in rough sets and allied fields are invited to propose to write chapters or chapter sections in about 200-500 words. Do mention relevant references. Please send the same to a.mani.cms@gmail.com and stefania.boffa@unimib.it

 

Target Audience:

All researchers, students and practitioners concerned with theoretical and practical problems in general rough sets and allied areas such as fuzzy sets, FCA, evidence theory, soft optimization, and artificial intelligence/machine learning in general.

Timeline: (Tentative):

Confirmation to Authors: 15^th August 2025+ (or earlier)

Submission Start: 30^th September 2025 (or earlier)

Submission Due: 15^th July 2026

Dynamic Reviews Due: 15^th August 2026

Final Version: 15^th September 2026

Book Submission: 30^th September 2026

 

Description of the Book

 

The handbook is intended to be a comprehensive up-to-date reference for general rough sets (and allied areas) – a subject that has progressed tremendously over the last four decades since its inception in the eighties of the last century in several directions. The research chapters critically cover basic to advanced topics in depth. A comprehensive array of examples with detailed descriptions of use cases for researchers, students, and practitioners of general rough sets and allied areas of artificial intelligence are part of the book.This is the first handbook in the subject that seeks to be a comprehensive, critical, and a unifying reference for all stakeholders. Further, itis intended to address a long-standing yet underexplored aspect of rough set theory: the role and nature of examples.

 

Topics ranging from the most basic to frontier areas are covered with particular attention to interconnections and gaps in research. New research problems, and directions are therefore part of the handbook.

 

Theoretical advances, and empirical developments, have not been in sync for a variety of reasons. The perspectives employed in algebraic, logical and topological studies do not necessarily align with the workarounds used in empirical studies, and applications. The latter practices often relate to problems of generality or ontological stability or meaning. Gaps in theoretical justifications of the formulas, measures or algorithms used in applications are common. So is the state of data-driven studies, and many ML algorithms. These are some reasons for a more systematic research handbook of general rough sets (and allied areas). The purpose of the edited volume is to address the issues mentioned to the extent possible, to serve as a standard reference for all stakeholders, to bring practitioners and theorists closer, and to improve the quality of the research discourse in the subject.

 

 

Section Editing Tasks (approximate):

Section: Basic Topics (A Mani, Davide Ciucci, Stefania Boffa)

Section: Partial and Total Algebraic Systems (A Mani, Davide Ciucci)

Section: PreTopological and Topological Models (A Mani, Chris Cornelis )

Section: Logics of RS (Davide Ciucci, Stefania Boffa, A Mani)

Section: Qualitative/Quantitative Probability and RS (A Mani, Yiyu Yao, )

Section: Knowledge Representation, and Causality (A Mani, Stefania Boffa)

Section: Soft Decision-Making and Classification (Yiyu Yao, Chris Cornelis )

Section: Hybrid Theories and Methods (Chris Cornelis, Stefania Boffa)

Section: General Rough Sets and Applied Philosophy (A Mani, Stefania Boffa, Yiyu Yao)

Section: Numeric Measures, Functions and Algorithms (A Mani, Sheela Ramanna, Yiyu Yao)

Section: Algorithms and Complexity (Beata Zielosko, Piotr Artemjew)

Section: General Rough ML Methods (Sheela Ramanna, Beata Zielosko, A Mani, Piotr Artemjew)

Section: Applications of General Rough Sets (Sheela Ramanna, Beata Zielosko, A Mani, Piotr Artemjew)

Section: Software for General Rough Sets (Chris Cornelis, A Mani )

 

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Table of Contents

Preface

Guide for Navigation

 

Section: Basic Topics

 

1. Information Tables

• Determinate and Indeterminate Tables

• Decision Tables

• Dynamic Tables

• Multi-Source Tables

• Information Tables and Logic

• Hybrid and Dynamic Tables

2. Relations and Approximations

 

2. Covers, and Approximations

 

3. Neighborhood-Based Approximations

 

4. Abstract, Partial, and Other Approximations

 

5. Basic Numeric Measures, and their Interpretation

 

6. Axiomatic Granules and Granulations

• Axiomatic

• Advanced Aspects

7. Precision-Based and Other Granulations

• Classical

• Precision-Based

• Dynamic/Adaptive

8. Reducts, and Feature Selection -1

• Attribute

• Partial

• Decision/Classification

• Object/Class-Specific

• Approximate

9. Rough Objects

10. Data Cleaning for Rough Computing

 

Section: Partial and Total Algebraic Systems of Rough Sets

Semantic vs Where-From Approach

11. Algebraic Systems of Classical Rough Sets

12. Algebraic Systems of Similarity RBRS

13. Algebraic Systems of Reflexive RBRS

14. Algebraic Systems of Generalized Transitive RBRS

15. Algebraic Systems of TransitiveRBRS

16. Algebraic Systems of Other RBRS

17. Algebraic Systems of Cover BRS-1

18. Algebraic Systems of Cover BRS-2

19. Algebraic Systems of Neighborhood BRS

20. Algebraic Systems of Modal Logics of RS

21. Algebraic Systems of Mereological RS

22. Algebraic Systems of Higher-Order Models

23. Algebraic Systems of Abstract RS

24. Discrete Dualities

25. Algebraic Systems of Paraconsistent Logics

26. Algebraic Systems of Multi-valued Logics

Note: + 2-3 Chapters on the rough object perspective.

Section: PreTopological and Topological Models

 

27. Pre-topologies of RS

28. Topologies of RS

29. Natural Dualities

30. Mereotopologies of RS

31. Near Sets-1

32. Near Sets-2

 

Section: Logics of General Rough Sets

 

33. Modal Logics

34. Kripke Models and Frames

35. Axiomatic Systems

36. Many-valued and Quantum Logics

37. Automata and RS

38. Paraconsistent Logics

39. Figures of Opposition

40. Other Logics of RS

41. Logics on Finite Alphabets

 

Section: Qualitative/Quantitative Probability and Rough Sets

 

42. Measure-theoretic Probability and RS

43. Stochastic Rough Probability Theories

44. Generalized Bayesian Reasoning

45. Connections with Subjective Probability Theories

46. Dependence and Deviant Probability Theories

47. Probabilistic Approximations

 

 

Section: Knowledge Representation, and Causality

 

48. Knowledge Representation in Classical RS

49. Knowledge Representation in General RS

50. Granular Knowledge Representation

51. Multivalued Knowledge Representation

52. Graded Knowledge Representation

53. Other Knowledge Representation Topics

 

54. Attribute-based Dependence

55. Dependence Between Objects Dependence

56. Dependence Between Processes

57. Theory-specific Dependence

58. Granular Dependence

59. Dependence and Commensurability

60. Higher Order Dependence

61. Graded Dependence

 

62. FCA and Classical Rough Sets

63. FCA and General Rough Sets

64. Vague Concepts

 

Section: Soft Decision-Making and Classification

 

65. Basic Three-Way Decision-Making

66. Three-Way Decision-Making and General RS

67. Hybrid Methods for 3-Way DM

68. 3-Way Decision Trees

69. Issues in 3-Way DM

70. Multilabel Classification-1

71. Multilabel Classification-2

72. Conflict Analysis and Decision-making

73. Multi-Source Decision-Making

74. Multi-Criteria Decision-Making

75. Flow Graphs

76. Hybrid Methods for Soft DM

 

Section: Hybrid Theories and Methods

 

77. Rough Fuzzy Sets

78. Fuzzy Rough Sets

79. L-fuzzy Sets and General Rough sets

80. Mereological Aspects of Hybrid L-Fuzzy Sets

81. Implications in Hybrid L-Fuzzy Sets

 

82. Evidence Theories and Rough Sets

83. Fusion and Related Issues

84. Evidential Rough Sets

85. Rough-Set valued Stochastic Models

86. Evidence Theories vs Probabilistic Rough Sets

 

87. Rough Neural Networks

88. Rough Neuro Fuzzy Computing

89. Genetic algorithms and Rough Sets

90. Explainable AI (XAI) and General Rough Sets

91. Other ML methods and Rough Sets

 

Section: General Rough Sets and Applied Philosophy

 

92. Numeric Rough Mereology

93. Applications, and Ontology for NRM

94. Spatial Mereology

95. RCC and Variants

96. Abstract Rough Mereological Models

97. Ontology

 

98. Domains of Discourse

99. Ontological Commitments

100. Distributed Cognition

101. Limits of Phenomenology

102. Various Isms of General Rough Sets

103. Other Topics

104. General Rough Sets as an Epistemic Tool

 

Section: Numeric Measures, Functions and Algorithms

 

105. Generalized Rough Inclusion Functions and Granularity

106. Membership Degrees

107. Basic Measures, and Entropies

108. Quality of Approximation

109. Optimization Problems and Measures

110. Classification Problems and Measures

 

Section: Algorithms and Complexity

(The chapters 111-117 in this section depend on the availability of suitable authors. They maybe reorganized from a functional perspective, meta-heuristics, and related issues.)

111*. Exact Algorithms for Decision Rules

112*. Approximate Algorithms of Decision Rules

113*. Algorithms for ReductComputation

114*. Approximate Algorithms for Reduct Computation

115*. Approximate Algorithms for Feature Selection.

116*. Metaheuristics and Related Issues

111. NP-Hard and NP-Complete Problems

112. Log-Linear Algorithms for General Rough Sets

113. Linear Algorithms for General Rough Sets

114. Quasi PTIME Algorithms for General Rough Sets

115. PTIME Algorithms for General Rough Sets

116. ZPPP Algorithms for General Rough Sets

117. BQP Algorithms for General Rough Sets

118. Algorithms for Reducts and Feature Selection

119. Algorithms for Classification and Decision Problems

120. Other Algorithms for General Rough Sets

 

Section: General Rough ML Methods

 

121. Rule Discovery, and KDD-1

122. Rule Discovery, and KDD-2

123. Rule Discovery, and KDD-3

 

124. Advanced Feature Selection-1

125. Advanced Feature Selection-2

126. Advanced Feature Selection-3

 

127. Hard Clustering and Rough Sets

128. Soft Clustering and Rough sets

129. General Rough Clustering

130. Meaning through General Rough Clustering

131. Commensurability in Soft Clustering

 

132. Rough Processes

133. C- Rough Randomness

 

134. Big Data -1

135. Big Data -2

136. Big Data -3

 

137. Applications to the Foundations of ML -1

 

Section: Applications of General Rough Sets

 

A. Applications to Education Research

138. For the Learning of Science

139. Mathematics Education Research

140. Modeling Ethnomathematics

141. Concept Inventories

142. Other Applications

 

 

B. Imaging, Video Analysis and Rough Sets

143. Imaging Tasks

144. Medical Imaging

145. Video Analysis

146. Fixed vs Moving Cameras

 

147. Near Sets based Methods-1

148. Near Sets based Methods-2

149. Near Sets based Methods-3

150. Other Methods

151. Overview of Applications to Medical Diagnostics

 

 

C. Data-driven Hybrid Applications

152. Data-driven Hybrid Applications-1

153. Data-driven Hybrid Applications-2

154. Data-driven Hybrid Applications-3

 

 

Section: Software for General Rough Sets

 

155. Best Practices

156. GNU/R Libraries

157. Good Reusable Code.

158. Other Libraries

 

 

 

 

_______________________________________________

 

A Mani

Senior Member, International Rough Set Society
AI Research Consultant (Mathematics)
Former Woman Scientist, Indian Statistical Institute, Kolkata
ORCID: 0000-0002-0880-1035

 

Stefania Boffa

Dipartimento di Business, Diritto, Economia e Consumi,

IULM University, 20143, Milan, Italy

ORCID: 0000-0002-4171-3459

 

Davide Ciucci

Dipartimento Di Informatica, Sistemistica E Comunicazione

Università degli Studi di Milano-Bicocca

ORCID: 0000-0002-8083-7809

 

Chris Cornelis

Dept. of Mathematics, Computer Science and Statistics

Ghent University, 9000 Gent, Belgium

ORCID: 0000−0002−6852−4041

 

Piotr Artemjew

Chair of Mathematical Methods of Informatics

Department of Mathematics and Computer Sciences

University of Warmia and Mazury

 

Sheela Ramanna

Department of Applied Computer Science

University of Winnipeg

Manitoba R3B 2E9, Canada

ORCID: 0000-0003-4169-6115

 

Yiyu Yao

Department of Computer Science

University of Regina

Regina, Saskatchewan, Canada S4S 0A2

ORCID: 0000-0001-6502-6226

 

Beata Zielosko

Institute of Computer Science

University of Silesia

Bȩdzińska 39, 41-200 Sosnowiec, Poland

ORCID: 0000-0003-3788-1094

 

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