Mining incomplete data with singleton, subset and concept probabilistic approximations

DOI10.1016/j.ins.2014.05.007zbMath1355.68254OpenAlexW2057720485MaRDI QIDQ507637

Jerzy W. Grzymala-Busse, Wojciech Rzasa, Patrick G. Clark

Publication date: 7 February 2017

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2014.05.007

zbMATH Keywords

incomplete dataprobabilistic approximationsextensions of probabilistic approximationssingleton, subset and concept probabilistic approximations

Mathematics Subject Classification ID

Reasoning under uncertainty in the context of artificial intelligence (68T37)

Related Items (6)

A Rough Set Approach to Incomplete Data ⋮ Consistency of incomplete data ⋮ Dominance-based rough set approach to incomplete ordered information systems ⋮ On rule acquisition in incomplete multi-scale decision tables ⋮ The rough membership functions on four types of covering-based rough sets and their applications ⋮ Extending characteristic relations on an incomplete data set by the three-way decision theory

Uses Software

• LERS

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• An interval set model for learning rules from incomplete information table
• Rough sets: some extensions
• Probabilistic rough set approximations
• Probabilistic approach to rough sets
• Parameterized rough set model using rough membership and Bayesian confirmation measures
• Variable precision rough set model
• Two views of the theory of rough sets in finite universes
• Relational interpretations of neighborhood operators and rough set approximation operators
• Rough set approach to incomplete information systems
• Rules in incomplete information systems
• The investigation of the Bayesian rough set model
• Generalized rough sets based on relations
• Knowledge acquisition in incomplete information systems: a rough set approach
• Foundations of Rule Learning
• Definability and Other Properties of Approximations for Generalized Indiscernibility Relations
• Rough sets: probabilistic versus deterministic approach
• Rough sets
• Generalized Probabilistic Approximations
• Transactions on Rough Sets I
• Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing
• Rough Sets and Current Trends in Computing
• Transactions on Rough Sets III
• Local and Global Approximations for Incomplete Data

This page was built for publication: Mining incomplete data with singleton, subset and concept probabilistic approximations