Journal ArticleDOI
Distributed approach for computing rough set approximations of big incomplete information systems
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TLDR
An efficient rough set theoretic (RST) algorithm is developed to compute the approximation space of the IIS, which addresses the incompleteness problem and a comparison test with similar approaches shows that it has superior performance.About:
This article is published in Information Sciences.The article was published on 2021-02-08. It has received 16 citations till now. The article focuses on the topics: Speedup & Rough set.read more
Citations
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Accurate Classification of COVID-19 Based on Incomplete Heterogeneous Data using a KNN Variant Algorithm
TL;DR: In this paper, a novel KNN variant (KNNV) algorithm is proposed to handle both incompleteness and heterogeneity, as well as to find an ideal value for K. The KNNV algorithm takes an incomplete, heterogeneous dataset, containing medical records of people, and identifies those cases with COVID-19.
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Improvement of Students’ Autonomous Learning Behavior by Optimizing Foreign Language Blended Learning Mode
TL;DR: In this paper , a blended learning model based on SPOC, which combines advantages of online and offline teaching, was developed to improve students' awareness and behavior of autonomous learning, as well as explore the effectiveness and optimization of this model effectively.
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A Theoretical Investigation Based on the Rough Approximations of Hypergraphs
TL;DR: In this paper , the notion of rough sets is applied to hypergraphs to introduce the novel concept of rough hypergraph based on rough relations, and the notions of isomorphism, conformality, linearity, duality, associativity, commutativity, distributivity, Helly property, and intersecting families are illustrated in rough graphs.
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MapReduce based parallel attribute reduction in Incomplete Decision Systems
TL;DR: In this article, MapReduce based parallel/distributed approaches for attribute reduction in large-scale incomplete decision systems (IDS) have been proposed to resolve the challenge of incompleteness with the existing Novel Granular Framework (NGF).
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A novel divergence measure in Dempster–Shafer evidence theory based on pignistic probability transform and its application in multi-sensor data fusion:
TL;DR: In this paper, the authors apply Dempster-Shafer (D-S) evidence theory in multi-sensor data fusion and show that it can effectively combine highly conflicting evidenc...
References
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Journal ArticleDOI
The WEKA data mining software: an update
TL;DR: This paper provides an introduction to the WEKA workbench, reviews the history of the project, and, in light of the recent 3.6 stable release, briefly discusses what has been added since the last stable version (Weka 3.4) released in 2003.
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Recurrent Neural Networks for Multivariate Time Series with Missing Values.
TL;DR: In this article, a deep learning model based on Gated Recurrent Unit (GRU) is proposed to exploit the missing values and their missing patterns for effective imputation and improving prediction performance.
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A survey on rough set theory and its applications
TL;DR: The basic concepts, operations and characteristics on the rough set theory are introduced, and then the extensions of rough set model, the situation of their applications, some application software and the key problems in applied research for the roughSet theory are presented.
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Implementing algorithms of rough set theory and fuzzy rough set theory in the R package “RoughSets”
Lala Septem Riza,Andrzej Janusz,Christoph Bergmeir,Chris Cornelis,Francisco Herrera,Dominik Śle¸zak,José Manuel Benítez +6 more
TL;DR: The package RoughSets, written mainly in the R language, provides implementations of methods from the rough set theory and fuzzy rough set theories for data modeling and analysis and should be considered as an alternative software library for analyzing data based on RST and FRST.
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Parallel attribute reduction in dominance-based neighborhood rough set
TL;DR: This paper investigates approaches to attribute reduction in parallel using dominance-based neighborhood rough sets (DNRS), which take into consideration the partial orders among numerical and categorical attribute values, and can be utilized in a multicriteria decision-making method.