A decision theoretic framework for approximating concepts
International Journal of Man-Machine Studies
Variable precision rough set model
Journal of Computer and System Sciences
Extensions and intentions in the rough set theory
Information Sciences: an International Journal
Axiomatics for fuzzy rough sets
Fuzzy Sets and Systems
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
A comparative study of fuzzy rough sets
Fuzzy Sets and Systems
A Generalized Definition of Rough Approximations Based on Similarity
IEEE Transactions on Knowledge and Data Engineering
Information Sciences—Informatics and Computer Science: An International Journal
Reduction and axiomization of covering generalized rough sets
Information Sciences: an International Journal
Constructive and axiomatic approaches of fuzzy approximation operators
Information Sciences—Informatics and Computer Science: An International Journal - Mining stream data
Margin based feature selection - theory and algorithms
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Information Sciences: an International Journal
On Three Types of Covering-Based Rough Sets
IEEE Transactions on Knowledge and Data Engineering
Learning fuzzy rules from fuzzy samples based on rough set technique
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Attribute reduction based on evidence theory in incomplete decision systems
Information Sciences: an International Journal
FRCT: fuzzy-rough classification trees
Pattern Analysis & Applications
A systematic study on attribute reduction with rough sets based on general binary relations
Information Sciences: an International Journal
On Minimal Rule Sets for Almost All Binary Information Systems
Fundamenta Informaticae - Half a Century of Inspirational Research: Honoring the Scientific Influence of Antoni Mazurkiewicz
Attribute reduction in decision-theoretic rough set models
Information Sciences: an International Journal
Neighborhood rough set based heterogeneous feature subset selection
Information Sciences: an International Journal
Editorial: Probabilistic rough sets: Approximations, decision-makings, and applications
International Journal of Approximate Reasoning
Probabilistic rough set approximations
International Journal of Approximate Reasoning
Relationship between generalized rough sets based on binary relation and covering
Information Sciences: an International Journal
Set-valued ordered information systems
Information Sciences: an International Journal
Knowledge Reduction of Covering Approximation Space
Transactions on Computational Science V
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
The model of fuzzy variable precision rough sets
IEEE Transactions on Fuzzy Systems
A comparison of pruning criteria for probability trees
Machine Learning
The investigation of the Bayesian rough set model
International Journal of Approximate Reasoning
Selecting discrete and continuous features based on neighborhood decision error minimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Reduction about approximation spaces of covering generalized rough sets
International Journal of Approximate Reasoning
Fuzzy preference based rough sets
Information Sciences: an International Journal
Positive approximation: An accelerator for attribute reduction in rough set theory
Artificial Intelligence
Soft fuzzy rough sets for robust feature evaluation and selection
Information Sciences: an International Journal
On the generalization of fuzzy rough sets
IEEE Transactions on Fuzzy Systems
Driver status recognition by neighborhood covering rules
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Dominance-based rough set model in intuitionistic fuzzy information systems
Knowledge-Based Systems
Covering based rough set approximations
Information Sciences: an International Journal
Extended rough set-based attribute reduction in inconsistent incomplete decision systems
Information Sciences: an International Journal
Relationships among generalized rough sets in six coverings and pure reflexive neighborhood system
Information Sciences: an International Journal
Attribute reduction of data with error ranges and test costs
Information Sciences: an International Journal
Attribute selection based on a new conditional entropy for incomplete decision systems
Knowledge-Based Systems
Related family: A new method for attribute reduction of covering information systems
Information Sciences: an International Journal
Rough matroids based on relations
Information Sciences: an International Journal
Rule extraction from support vector machines based on consistent region covering reduction
Knowledge-Based Systems
Quick attribute reduction in inconsistent decision tables
Information Sciences: an International Journal
Nullity-based matroid of rough sets and its application to attribute reduction
Information Sciences: an International Journal
Characteristic matrix of covering and its application to Boolean matrix decomposition
Information Sciences: an International Journal
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Rough set theory has been extensively discussed in the domain of machine learning and data mining. Pawlak's rough set theory offers a formal theoretical framework for attribute reduction and rule learning from nominal data. However, this model is not applicable to numerical data, which widely exist in real-world applications. In this work, we extend this framework to numerical feature spaces by replacing partition of universe with neighborhood covering and derive a neighborhood covering reduction based approach to extracting rules from numerical data. We first analyze the definition of covering reduction and point out its advantages and disadvantages. Then we introduce the definition of relative covering reduction and develop an algorithm to compute it. Given a feature space, we compute the neighborhood of each sample and form a neighborhood covering of the universe, and then employ the algorithm of relative covering reduction to the neighborhood covering, thus derive a minimal covering rule set. Some numerical experiments are presented to show the effectiveness of the proposed technique.