Literature DB >> 30524217

Rough sets: past, present, and future.

Andrzej Skowron1,2, Soma Dutta3,4.   

Abstract

Introduction of rough sets by Professor Zdzisław Pawlak has completed 35 years. The theory has already attracted the attention of many researchers and practitioners, who have contributed essentially to its development, from all over the world. The methods, developed based on rough set theory alone or in combination with other approaches, found applications in many areas. In this article, we outline some selected past and present research directions of rough sets. In particular, we emphasize the importance of searching strategies for relevant approximation spaces as the basic tools in achieving computational building blocks (granules or patterns) required for approximation of complex vague concepts. We also discuss new challenges related to problem solving by intelligent systems (IS) or complex adaptive systems (CAS). The concern is to control problems using interactive granular computing, an extension of the rough set approach, for effective realization of computations realized in IS or CAS. These challenges are important for the development of natural computing too.

Entities:  

Keywords:  (Approximate) Boolean reasoning; Adaptive judgment; Complex adaptive system; Granular computing; Interaction; Natural computing; Rough set

Year:  2018        PMID: 30524217      PMCID: PMC6244804          DOI: 10.1007/s11047-018-9700-3

Source DB:  PubMed          Journal:  Nat Comput        ISSN: 1567-7818            Impact factor:   1.690


  2 in total

Review 1.  From computing with numbers to computing with words. From manipulation of measurements to manipulation of perceptions.

Authors:  L A Zadeh
Journal:  Ann N Y Acad Sci       Date:  2001-04       Impact factor: 5.691

2.  Interactive computations: toward risk management in interactive intelligent systems.

Authors:  Andrzej Skowron; Andrzej Jankowski
Journal:  Nat Comput       Date:  2015-02-11       Impact factor: 1.690

  2 in total
  4 in total

Review 1.  Managing Boundary Uncertainty in Diagnosing the Patients of Rural Area Using Fuzzy and Rough Set.

Authors:  Sayan Das; Jaya Sil
Journal:  J Healthc Inform Res       Date:  2022-01-03

2.  Interpretable machine learning identifies paediatric Systemic Lupus Erythematosus subtypes based on gene expression data.

Authors:  Jennifer R S Meadows; Jan Komorowski; Sara A Yones; Alva Annett; Patricia Stoll; Klev Diamanti; Linda Holmfeldt; Carl Fredrik Barrenäs
Journal:  Sci Rep       Date:  2022-05-06       Impact factor: 4.996

3.  Interpretable Machine Learning Reveals Dissimilarities Between Subtypes of Autism Spectrum Disorder.

Authors:  Mateusz Garbulowski; Karolina Smolinska; Klev Diamanti; Gang Pan; Khurram Maqbool; Lars Feuk; Jan Komorowski
Journal:  Front Genet       Date:  2021-02-25       Impact factor: 4.599

4.  Rough set approximations based on a matroidal structure over three sets.

Authors:  Gang Wang; Hua Mao; Chang Liu; Zhiming Zhang; Lanzhen Yang
Journal:  Appl Intell (Dordr)       Date:  2022-10-06       Impact factor: 5.019

  4 in total

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