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Rough Sets Handling Missing Values Probabilistically Interpreted
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/3-540-45554-x_25openalex-snapshot
Valued Tolerance and Decision Rules
inferredcites →https://doi.org/10.1007/3-540-54563-8openalex-snapshot
Methodologies for Intelligent Systems
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On the unknown attribute values in learning from examples
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Some Issues on Nondeterministic Knowledge Bases with Incomplete and Selective Information
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Imperfect knowledge in databases
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An Algorithm for Finding Equivalence Relations from Tables with Non-deterministic Information
inferredcites →https://doi.org/10.1007/978-3-540-48061-7_11openalex-snapshot
On the Extension of Rough Sets under Incomplete Information
inferredcites →https://doi.org/10.1007/978-3-540-88425-5openalex-snapshot
Rough Sets and Current Trends in Computing
inferredcites →https://doi.org/10.1007/978-3-7908-1840-6openalex-snapshot
Rough Set Methods and Applications
inferredcites →https://doi.org/10.1007/978-3-7908-1840-6_12openalex-snapshot
Data Mining in Incomplete Information Systems from Rough Set Perspective
inferredcites →https://doi.org/10.1007/b72293openalex-snapshot
New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
inferredcites →https://doi.org/10.1016/0895-7177(89)90373-7openalex-snapshot
Rough classification in incomplete information systems
inferredcites →https://doi.org/10.1016/0898-1221(95)90258-9openalex-snapshot
Foundations of databases
inferredcites →https://doi.org/10.1016/s0004-3702(01)00147-3openalex-snapshot
Rough approximation quality revisited
inferredcites →https://doi.org/10.1016/s0020-0255(98)10065-8openalex-snapshot
Rules in incomplete information systems
inferredcites →https://doi.org/10.1109/69.506705openalex-snapshot
Current approaches to handling imperfect information in data and knowledge bases
inferredcites →https://doi.org/10.1111/0824-7935.00162openalex-snapshot
Incomplete Information Tables and Rough Classification
inferredcites →https://doi.org/10.1145/1634.1886openalex-snapshot
Incomplete Information in Relational Databases
inferredcites →https://doi.org/10.2307/253812openalex-snapshot
Uncertainty Management in Information Systems: From Needs to Solutions
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/11548669openalex-snapshot
Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing
inferred← citeshttps://doi.org/10.1007/11548706openalex-snapshot
Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing
inferred← citeshttps://doi.org/10.1007/11795131_22openalex-snapshot
Applying Rough Sets to Data Tables Containing Possibilistic Information
inferred← citeshttps://doi.org/10.1007/11908029_24openalex-snapshot
Applying Rough Sets to Data Tables Containing Imprecise Information Under Probabilistic Interpretation
inferred← citeshttps://doi.org/10.1007/11908029_27openalex-snapshot
Local and Global Approximations for Incomplete Data
inferred← citeshttps://doi.org/10.1007/978-3-030-22815-6_13openalex-snapshot
Rough Sets Based on Possible Indiscernibility Relations in Incomplete Information Tables with Continuous Values
inferred← citeshttps://doi.org/10.1007/978-3-030-31129-2_2openalex-snapshot
Rough Sets Based on Possibly Indiscernible Classes in Incomplete Information Tables with Continuous Values
inferred← citeshttps://doi.org/10.1007/978-3-031-46781-3_25openalex-snapshot
Extending Kryszkiewicz’s Formula of Missing Values in Terms of Lipski’s Approach
inferred← citeshttps://doi.org/10.1007/978-3-031-50959-9_12openalex-snapshot
Kryszkiewicz’s Relation for Indiscernibility of Objects in Data Tables Containing Missing Values
inferred← citeshttps://doi.org/10.1007/978-3-031-65665-1_10openalex-snapshot
Dealing with Missing Values Meaning Unknown in Probabilistic Approximations
inferred← citeshttps://doi.org/10.1007/978-3-319-02931-3_45openalex-snapshot
A Study of Incomplete Data – A Review
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A Rough Set Approach to Incomplete Data
inferred← citeshttps://doi.org/10.1007/978-3-319-91476-3_38openalex-snapshot
Attribute Reduction of Set-Valued Decision Information System
inferred← citeshttps://doi.org/10.1007/978-3-319-91476-3_41openalex-snapshot
Rules Induced from Rough Sets in Information Tables with Continuous Values
inferred← citeshttps://doi.org/10.1007/978-3-319-99368-3_25openalex-snapshot
Rule Induction Based on Indiscernible Classes from Rough Sets in Information Tables with Continuous Values
inferred← citeshttps://doi.org/10.1007/978-3-540-24844-6_7openalex-snapshot
Rough Set Approach to Incomplete Data
inferred← citeshttps://doi.org/10.1007/978-3-540-71663-1_11openalex-snapshot
Lower and Upper Approximations in Data Tables Containing Possibilistic Information
inferred← citeshttps://doi.org/10.1007/978-3-540-73451-2_20openalex-snapshot
Applying Rough Sets to Data Tables Containing Missing Values
inferred← citeshttps://doi.org/10.1007/978-3-540-73729-2_27openalex-snapshot
Applying Rough Sets to Information Tables Containing Probabilistic Values
inferred← citeshttps://doi.org/10.1007/978-3-540-85064-9_2openalex-snapshot
Local and Global Approximations for Incomplete Data
inferred← citeshttps://doi.org/10.1007/978-3-540-87563-5_11openalex-snapshot
Applying Rough Sets to Information Tables Containing Possibilistic Values
inferred← citeshttps://doi.org/10.1007/978-3-642-02962-2_10openalex-snapshot
Rough Sets under Non-deterministic Information
inferred← citeshttps://doi.org/10.1007/978-3-642-05177-7_17openalex-snapshot
Increasing Incompleteness of Data Sets—A Strategy for Inducing Better Rule Sets
inferred← citeshttps://doi.org/10.1007/978-3-642-21881-1_8openalex-snapshot
Dual Rough Approximations in Information Tables with Missing Values
inferred← citeshttps://doi.org/10.1007/978-3-642-24425-4_1openalex-snapshot
Mining Incomplete Data—A Rough Set Approach
inferred← citeshttps://doi.org/10.1007/978-3-642-30344-9_3openalex-snapshot
Rough Sets: From Rudiments to Challenges
inferred← citeshttps://doi.org/10.1007/978-3-642-35455-7_18openalex-snapshot
Novel Granular Framework for Attribute Reduction in Incomplete Decision Systems
inferred← citeshttps://doi.org/10.1007/s10844-010-0121-8openalex-snapshot
A review and comparison of strategies for handling missing values in separate-and-conquer rule learning
inferred← citeshttps://doi.org/10.1016/j.ins.2006.06.007openalex-snapshot
Rough sets and Boolean reasoning
inferred← citeshttps://doi.org/10.1016/j.knosys.2020.106677openalex-snapshot
MapReduce based parallel attribute reduction in Incomplete Decision Systems
inferred← citeshttps://doi.org/10.1049/trit.2019.0025openalex-snapshot
Rule induction based on rough sets from information tables having continuous domains
inferred← citeshttps://doi.org/10.1080/17451000.2013.798898openalex-snapshot
Twofold rough approximations under incomplete information
inferred← citeshttps://doi.org/10.1109/fuzzy.2006.1682026openalex-snapshot
Rough Sets Approximations to Possibilistic Information
inferred← citeshttps://doi.org/10.1109/fuzzy.2008.4630442openalex-snapshot
Rough sets approximations in data tables containing missing values
inferred← citeshttps://doi.org/10.1109/grc.2010.139openalex-snapshot
Two Rough Approximations for Information Tables Containing Missing Values
inferred← citeshttps://doi.org/10.1109/grc.2011.6122596openalex-snapshot
Mining data with numerical attributes and missing attribute values — A rough set approach
inferred← citeshttps://doi.org/10.1109/hsi.2009.5090982openalex-snapshot
A valued tolerance approach to missing attribute values in data mining
inferred← citeshttps://doi.org/10.1109/iceoe.2011.6013488openalex-snapshot
Research of electronic medical record data mining method based on variable precision rough set
inferred← citeshttps://doi.org/10.1109/ichit.2006.21openalex-snapshot
A New Approach to Computing Weighted Attributes Values in Incomplete Information Systems
inferred← citeshttps://doi.org/10.1109/ichit.2006.253622openalex-snapshot
A New Approach to Computing Weighted Attributes Values in Incomplete Information Systems
inferred← citeshttps://doi.org/10.1109/ifsa-nafips.2013.6608381openalex-snapshot
Rule induction based on rough sets from information tables containing possibilistic information
inferred← citeshttps://doi.org/10.1109/ismvl.2009.1openalex-snapshot
Applying Rough Sets to Information Tables Containing Missing Values
inferred← citeshttps://doi.org/10.1109/nafips.2012.6290994openalex-snapshot
Dual rough approximations in possibilistic information systems
inferred← citeshttps://doi.org/10.1155/2007/58248openalex-snapshot
A Modified Rough Set Approach to Incomplete Information Systems
inferred← citeshttps://doi.org/10.3844/jcssp.2011.216.224openalex-snapshot
Predicting Missing Attribute Values Using k-Means Clustering
inferred← citeshttps://doi.org/10.4018/ijdsst.2017040101openalex-snapshot
A Comparative Study Based on Rough Set and Classification Via Clustering Approaches to Handle Incomplete Data to Predict Learning Styles
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