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2022 | Buch

Zdzisław Pawlak and Our Journey with Rough Sets Kapitel lesen Erstes Kapitel lesen

Transactions on Rough Sets XXIII

herausgegeben von: James F. Peters, Andrzej Skowron, Rabi Nanda Bhaumik, Sheela Ramanna

Verlag: Springer Berlin Heidelberg

Buchreihe : Lecture Notes in Computer Science

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

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 XXIII 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.

Inhaltsverzeichnis

Frontmatter

FRSA 2021 Conference Papers

Frontmatter
Zdzisław Pawlak and Our Journey with Rough Sets
Abstract
The authors trace their journey with rough sets since their first interactions with Z. Pawlak. The article is a narration of how work on rough sets was initiated in India, and how it continues to thrive in research groups connected to the authors and others in the country.
Mohua Banerjee, Mihir K. Chakraborty
Fuzzy -Cut in Rough Sets and Its Application
Abstract
This work indicates application of the notion of fuzzy \(\alpha \)-cut in rough set theory and studies the properties of Gödel-like arrow in details.
Purbita Jana
Named Entity Recognition on CORD-19 Bio-Medical Dataset with Tolerance Rough Sets
Abstract
Biomedical named entity recognition is becoming increasingly important to biomedical research due to a proliferation of articles and also due to the current pandemic disease. This paper addresses the task of automatically finding and recognizing biomedical entity types related to COVID (e.g., virus, cell, therapeutic) with tolerance rough sets. The task includes i) extracting nouns and their co-occurring contextual patterns from a large BioNER dataset related to COVID-19 and, ii) annotating unlabelled data with a semi-supervised learning algorithm using co-occurence statistics. 465,250 noun phrases and 6,222,196 contextual patterns were extracted from 29,500 articles using natural language text processing methods. Three categories were successfully classified at this time: virus, cell and therapeutic. Early precision@N results demonstrate that our proposed tolerant pattern learner (TPL) is able to constrain concept drift in all 3 categories during the iterative learning process.
Seeratpal Jaura, Sheela Ramanna
Granularity and Rational Approximation: Rethinking Graded Rough Sets
Abstract
The concept of rational discourse is typically determined by subjective, normative, and rule based constraints in the context under consideration. It is typically determined by related ontologies, and coherence between associated concepts employed in the discourse. Classical rough approximations, and variants of variable precision rough sets (VPRS) including graded rough sets embody at least some aspects of potentially useful concepts of rational approximation, but can be very lacking in application contexts, and rough set theoretical frameworks for cluster validation. While the literature on knowledge from general rough perspectives is rich and diverse, not much work has been done from the perspective of rationality in explicit terms. In this research, the gap is addressed by the present author in variants of high granular partial algebras. Specifically, the nature of optimal concepts of rational approximations is examined, and formalized by her in such frameworks. Graded rough sets are generalized from a granular perspective, and the compatibility of the introduced concepts are studied over it. Further aspects of algebraic semantics of granular graded rough sets are examined. Some incorrect results in graded rough sets in the literature are also corrected.
A. Mani
MADM Strategies Based on Arithmetic and Geometric Mean Operator Under Rough-Bipolar Neutrosophic Set Environment
Abstract
The main focus of this paper is to introduce some aggregation operators namely, Rough-Bipolar Neutrosophic Arithmetic Mean (RBNAM) operator and Rough-Bipolar Neutrosophic Geometric Mean (RBNGM) operator under Rough-Bipolar Neutrosophic Set (RBNS) environment. Besides, we present the concept of score and accuracy functions under the RBNS environment. Further, we propose two multi-attribute decision-making (MADM) strategies based on RBNAM operator and RBNGM operator respectively under the RBNS environment. Finally, we provide a real-life numerical example to validate the proposed MADM strategy.
Surapati Pramanik, Suman Das, Rakhal Das, Binod Chandra Tripathy
Single-Valued Neutrosophic Rough Continuous Mapping via Single-Valued Neutrosophic Rough Topological Space
Abstract
In this article an attempt is made to introduce and study the notion of single-valued neutrosophic rough continuous mapping, single-valued neutrosophic rough compactness via single-valued neutrosophic rough topological spaces (SVNRTS). By defining the concept of single-valued neutrosophic rough continuous function, single-valued neutrosophic rough compactness, we formulate and discuss several interesting results on SVNRTSs.
Binod Chandra Tripathy, Suman Das, Rakhal Das

Regular Paper

Frontmatter
The RSDS - Bibliographic Database for Rough Sets and Related Fields
Abstract
This paper provides an overview of the Rough Set Database System (the RSDS for short) for creating bibliographies on rough sets and related fields, as well as sharing and analysis. The current version of the RSDS includes a number of modifications, extensions and functional improvements compared to the previous versions of this system. The system was made in the client-server technology. Currently, the RSDS contains over 38 540 entries from nearly 42 860 authors. This system works on any computer connected to the Internet and is available at http://​rsds.​ur.​edu.​pl.
Zbigniew Suraj, Piotr Grochowalski

Dissertations

Frontmatter
Selected Aspects of Interactive Feature Extraction
Abstract
In the presented study, the problem of interactive feature extraction, i.e., supported by interaction with users, is discussed, and several innovative approaches to automating feature creation and selection are proposed. The current state of knowledge on feature extraction processes in commercial applications is shown. The problems associated with processing big data sets as well as approaches to process high-dimensional time series are discussed. The introduced feature extraction methods were subjected to experimental verification on real-life problems and data. Besides the experimentation, the practical case studies and applications of developed techniques in selected scientific projects are shown.
Feature extraction addresses the problem of finding the most compact and informative data representation resulting in improved efficiency of data storage and processing, facilitating the subsequent learning and generalization steps. Feature extraction not only simplifies the data representation but also enables the acquisition of features that can be further easily utilized by both analysts and learning algorithms. In its most common flow, the process starts from an initial set of measured data and builds derived features intended to be informative and non-redundant. Logically, there are two phases of this process: the first is the construction of the new attributes based on original data (sometimes referred to as feature engineering), the second is a selection of the most important among the attributes (sometimes referred to as feature selection). There are many approaches to feature creation and selection that are well-described in the literature. Still, it is hard to find methods facilitating interaction with users, which would take into consideration users’ knowledge about the domain, their experience, and preferences.
In the study on the interactiveness of the feature extraction, the problems of deriving useful and understandable attributes from raw sensor readings and reducing the amount of those attributes to achieve possibly simplest, yet accurate, models are addressed. The proposed methods go beyond the current standards by enabling a more efficient way to express the domain knowledge associated with the most important subsets of attributes. The proposed algorithms for the construction and selection of features can use various forms of information granulation, problem decomposition, and parallelization. They can also tackle large spaces of derivable features and ensure a satisfactory (according to a given criterion) level of information about the target variable (decision), even after removing a substantial number of features.
The proposed approaches have been developed based on the experience gained in the course of several research projects in the fields of data analysis and processing multi-sensor data streams. The methods have been validated in terms of the quality of the extracted features, as well as throughput, scalability, and robustness of their operation. The discussed methodology has been verified in open data mining competitions to confirm its usefulness.
Marek Grzegorowski
A Study of Algebraic Structures and Logics Based on Categories of Rough Sets

The theory of rough sets has been studied extensively, both from foundation and application points of view, since its introduction by Pawlak in 1982. On the foundations side, a substantial part of work on rough set theory involves the study of its algebraic aspects and logics. The present work is in this direction, initiated through the study of categories of rough sets.

Starting from two categories RSC and ROUGH of rough sets, it is shown that they are equivalent. Moreover, RSC, and thus ROUGH, are found to be a quasitopos, a structure slightly weaker than topos. The construction is then lifted to a more general set-up to give the category RSC(\(\mathscr {C}\)) with an arbitrary non-degenerate topos \(\mathscr {C}\) serving as a ‘base’, just as sets constitute a base for defining rough sets.

The category-theoretic study gives rise to two directions of work. In one direction of work, a particular example of RSC(\(\mathscr {C}\)) when \(\mathscr {C}\) is the topos of monoid actions on sets is considered. It yields the monoid actions on rough sets and that of transformation semigroups (ts) for rough sets, leading to decomposition results. A semiautomaton for rough sets is also defined.

In the other direction, we incorporate Iwinski’s notion of ‘relative rough complementation’ in the internal algebra of the quasitopos RSC(\(\mathscr {C}\)). This results in the introduction of two new classes of algebraic structures with two negations, namely contrapositionally complemented pseudo-Boolean algebra (ccpBa) and contrapositionally \({\vee }\) complemented pseudo-Boolean algebra (c\(\vee \)cpBa). Examples of ccpBas and c\(\vee \)cpBas are developed, comparison with existing algebras is done and representation theorems are established.

The logics ILM and ILM-\(\vee \) corresponding to ccpBas and c\({\vee }\)cpBas respectively are defined, and different relational semantics are obtained. It is shown that ILM is a proper extension of a variant JP\('\) of Peirce’s logic, defined by Segerberg in 1968. The inter-relationship between relational semantics and the algebraic semantics of ILM and ILM-\({\vee }\) are investigated. Lastly, in the line of Dunn’s study of logics, the two negations are expressed without the help of the connective of implication, and the resulting logical and algebraic structures are also studied.

Anuj Kumar More
Backmatter
Metadaten
Titel
Transactions on Rough Sets XXIII
herausgegeben von
James F. Peters
Andrzej Skowron
Rabi Nanda Bhaumik
Sheela Ramanna
Copyright-Jahr
2022
Verlag
Springer Berlin Heidelberg
Electronic ISBN
978-3-662-66544-2
Print ISBN
978-3-662-66543-5
DOI
https://doi.org/10.1007/978-3-662-66544-2

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