Rough Sets

International Joint Conference, IJCRS 2019, Debrecen, Hungary, June 17–21, 2019, Proceedings

Häftad, Engelska, 2019

Av Tamás Mihálydeák, Fan Min, Guoyin Wang, Mohua Banerjee, Ivo Düntsch, Zbigniew Suraj, Davide Ciucci, Fan Mihálydeák, Tamás

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This LNAI 11499 constitutes the proceedings of the International Joint Conference on Rough Sets, IJCRS 2019, held in Debrecen, Hungary, in June 2019.The 41 full papers were carefully reviewed and selected from 71 submissions. The IJCRS conferences aim at bringing together experts from universities and research centers as well as the industry representing ﬁelds of research in which theoretical and applicational aspects of rough set theory already ﬁnd or may potentially ﬁnd usage. The papers are grouped in topical sections on core rough set models and methods; related methods and hybridization; areas of application.

Produktinformation

Utgivningsdatum2019-06-09
Mått155 x 235 x undefined mm
FormatHäftad
SpråkEngelska
SerieLecture Notes in Artificial Intelligence
Antal sidor550
FörlagSpringer Nature Switzerland AG
ISBN9783030228149

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Core Rough Set Models and Methods.- An Application of Bayesian Confirmation Theory for Three-Way Decision.- Concept Approximation Based on Rough Sets and Judgment.- Rough Sets and the Algebra of Conditional Logic.- Rough Sets Defined by Multiple Relations.- On the Roughly Continuous Real Functions.- On topologies defined by binary relations in rough sets.- Iterative Set Approximations Based on Tolerance Relation.- Approximation Based on Representatives.- Local search for attribute reduction.- Rough Matroids Based on Dual Approximation Operators.- Studies on Reducing the Necessary Data Size for Rule Induction from the Decision Table by STRIM.- Rough Approximations on Two Universes under a Mapping.- Rough Sets Based on Possible Indiscernibility Relations in Incomplete Information Tables with Continuous Values.- The Prototype View of Concepts.- A Three-Way Clustering Algorithm via Decomposing Similarity Matrices for Multi-View Data with Noise.- Related Methods and Hybridization.- A Scalable Approach to Fuzzy Rough Nearest Neighbour Classification with Ordered Weighted Averaging Operators.- Learning Multi-granular Features for Harvesting Knowledge from Free Text.- Building a Framework of Rough Inclusion Functions by Means of Computerized Proof Assistant.- Membrane Systems and Multiset Approximation: The Cases of Inner and Boundary Rule Application.- Soft Petri Net.- Approximations induced by tolerance relations.- Three-Way Classification: Ambiguity and Abstention in Machine Learning.- Concepts Approximation Through Dialogue With User.- A Dynamic Dominance-based Rough Set Approach for Processing Ordered Data.- CSLI: Cost-sensitive collaborative filtering with local information embedding.- Attribute reduction based on optimistic multi-granulation information Systems.- Constructing the Optimal Approximation Sets of Rough Sets in Multi-granularity Spaces.- Discovering Flow Graphs from Data Tables Using the Classification and Prediction Software System (CLAPSS).- Methods to Edit Multi-label Training Sets Using Rough Sets Theory.- Areas of Applications.- The Impact of Rough Set Conferences.- Multivariate Ovulation Window Detection at OvuFriend.- Incremental Sequential Three-Way Decision Using a Deep Stacked Autoencoder.- Three-way Decision Collaborative Recommendation Algorithm Based on User Reputation.- Multi-Graded Hybrid MRDM Model for Assisting Financial Performance Evaluation Decisions: A Preliminary Work.- 3D Face Recognition Based on Hybrid Data.- Rough Sets and Local Texture Features for Diagnosis of Connective Tissue Disorders.- Developing Pricing Models for Online Art Sales Using Text Analytics.- Hardware Implementation on Field Programmable Gate Array of Two-Stage Algorithm for Rough Set Reduct Generation.- A Multi-Granularity Representation Learning Framework for User Identification Across Social Networks.- A Robust Long-term Pedestrian Tracking-by-Detection Algorithm Based on Three-way Decision.- A Bibliometric Profileof Research on Rough Sets.