Comparison of rough-set and statistical methods in inductive learning
International Journal of Man-Machine Studies
IEEE Transactions on Software Engineering
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Variable precision rough set model
Journal of Computer and System Sciences
From rough set theory to evidence theory
Advances in the Dempster-Shafer theory of evidence
Advances in the Dempster-Shafer theory of evidence
Fuzzy independence and extended conditional probability
Information Sciences: an International Journal
An introduction to Kolmogorov complexity and its applications (2nd ed.)
An introduction to Kolmogorov complexity and its applications (2nd ed.)
Uncertainly measures of rough set prediction
Artificial Intelligence
Multivalued dependencies and a new normal form for relational databases
ACM Transactions on Database Systems (TODS)
Rough set algorithms in classification problem
Rough set methods and applications
Various approaches to reasoning with frequency based decision reducts: a survey
Rough set methods and applications
SPARTAN: a model-based semantic compression system for massive data tables
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Machine Learning
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Information Theory and Reliable Communication
Information Theory and Reliable Communication
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
Rough set methods in feature selection and recognition
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
Classification Algorithms Based on Linear Combinations of Features
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Stable independence and complexity of representation
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Probabilistic Conditional Independence Structures: With 42 Illustrations (Information Science and Statistics)
Very large Bayesian multinets for text classification
Future Generation Computer Systems
Interactive Gene Clustering--A Case Study of Breast Cancer Microarray Data
Information Systems Frontiers
A First Course in Information Theory (Information Technology: Transmission, Processing and Storage)
A First Course in Information Theory (Information Technology: Transmission, Processing and Storage)
Computational Methods of Feature Selection (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series)
On Efficient Handling of Continuous Attributes in Large Data Bases
Fundamenta Informaticae
Discovery of multivalued dependencies from relations
Intelligent Data Analysis
Probabilistic graphical models and their role in databases
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Approximation Degrees in Decision Reduct-Based MRI Segmentation
FBIT '07 Proceedings of the 2007 Frontiers in the Convergence of Bioscience and Information Technologies
Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches
Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
Generalized theory of uncertainty (GTU)-principal concepts and ideas
Computational Statistics & Data Analysis
The investigation of the Bayesian rough set model
International Journal of Approximate Reasoning
A general definition of an attribute reduct
RSKT'07 Proceedings of the 2nd international conference on Rough sets and knowledge technology
Roughfication of numeric decision tables: the case study of gene expression data
RSKT'07 Proceedings of the 2nd international conference on Rough sets and knowledge technology
Order based genetic algorithms for the search of approximate entropy reducts
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Relevant attribute discovery in high dimensional data: application to breast cancer gene expressions
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
Mining of MicroRNA expression data—a rough set approach
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
On the implication problem for probabilistic conditional independency
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Attribute reduction and optimal decision rules acquisition for continuous valued information systems
Information Sciences: an International Journal
Rough Sets and Functional Dependencies in Data: Foundations of Association Reducts
Transactions on Computational Science V
Feature selection for multi-label naive Bayes classification
Information Sciences: an International Journal
Attribute selection with fuzzy decision reducts
Information Sciences: an International Journal
Selecting discrete and continuous features based on neighborhood decision error minimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Generating probabilistic Boolean networks from a prescribed stationary distribution
Information Sciences: an International Journal
Feature Selection via Maximizing Fuzzy Dependency
Fundamenta Informaticae
Weighted nearest neighbor classification via maximizing classification consistency
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
The superiority of three-way decisions in probabilistic rough set models
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
Distance: A more comprehensible perspective for measures in rough set theory
Knowledge-Based Systems
A measurement theory view on the granularity of partitions
Information Sciences: an International Journal
NMGRS: Neighborhood-based multigranulation rough sets
International Journal of Approximate Reasoning
Attribute Reduction in Formal Contexts: A Covering Rough Set Approach
Fundamenta Informaticae - Knowledge Technology
An Improved Axiomatic Definition of Information Granulation
Fundamenta Informaticae
The problem of finding the sparsest bayesian network for an input data set is NP-Hard
ISMIS'12 Proceedings of the 20th international conference on Foundations of Intelligent Systems
Information Sciences: an International Journal
Relational Operations and Uncertainty Measure in Rough Relational Database
Fundamenta Informaticae
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Bayesian networks provide the means for representing probabilistic conditional independence. Conditional independence is widely considered also beyond the theory of probability, with linkages to, e.g. the database multi-valued dependencies, and at a higher abstraction level of semi-graphoid models. The rough set framework for data analysis is related to the topics of conditional independence via the notion of a decision reduct, to be considered within a wider domain of the feature selection. Given probabilistic version of decision reducts equivalent to the data-based Markov boundaries, the studies were also conducted for other criteria of the rough-set-based feature selection, e.g. those corresponding to the multi-valued dependencies. In this paper, we investigate the degrees of approximate conditional dependence, which could be a topic corresponding to the well-known notions such as conditional mutual information and polymatroid functions, however, with many practically useful approximate conditional independence models unmanageable within the information theoretic framework. The major paper's contribution lays in extending the means for understanding the degrees of approximate conditional dependence, with appropriately generalized semi-graphoid properties formulated and with the mathematical soundness of the Bayesian network-like representation of the approximate conditional independence statements thoroughly proved. As an additional contribution, we provide a case study of the approximate conditional independence model, which would not be manageable without the above-mentioned extensions.