A new version of the rule induction system LERS
Fundamenta Informaticae
Uncertainly measures of rough set prediction
Artificial Intelligence
Relational interpretations of neighborhood operators and rough set approximation operators
Information Sciences—Informatics and Computer Science: An International Journal
Rough set approach to incomplete information systems
Information Sciences: an International Journal
Information Sciences: an International Journal
Rules in incomplete information systems
Information Sciences: an International Journal
A Handwritten Numeral Character Classification Using Tolerant Rough Set
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Mathematical Foundations
Rough Sets: Mathematical Foundations
Rough set methods in feature selection and recognition
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
Approaches to knowledge reduction based on variable precision rough set model
Information Sciences—Informatics and Computer Science: An International Journal - Mining stream data
Entropies and Co-Entropies of Coverings with Application to Incomplete Information Systems
Fundamenta Informaticae - New Frontiers in Scientific Discovery - Commemorating the Life and Work of Zdzislaw Pawlak
Information Sciences: an International Journal
Comparison between different kinds of approximations by using a family of binary relations
Knowledge-Based Systems
Exploring the boundary region of tolerance rough sets for feature selection
Pattern Recognition
Discovering patterns of missing data in survey databases: An application of rough sets
Expert Systems with Applications: An International Journal
Financial time-series analysis with rough sets
Applied Soft Computing
Information Sciences: an International Journal
Information Entropy and Granulation Co---Entropy of Partitions and Coverings: A Summary
Transactions on Rough Sets X
Information Sciences: an International Journal
Three-way decisions with probabilistic rough sets
Information Sciences: an International Journal
Covering Based Approaches to Rough Sets and Implication Lattices
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Information-theoretic measures of uncertainty for rough sets and rough relational databases
Information Sciences: an International Journal
Interval-valued fuzzy-rough feature selection in datasets with missing values
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
A rough set approach for selecting clustering attribute
Knowledge-Based Systems
Positive approximation: An accelerator for attribute reduction in rough set theory
Artificial Intelligence
Approximation reduction in inconsistent incomplete decision tables
Knowledge-Based Systems
Soft fuzzy rough sets for robust feature evaluation and selection
Information Sciences: an International Journal
Classification of dynamics in rough sets
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Sequential covering rule induction algorithm for variable consistency rough set approaches
Information Sciences: an International Journal
The superiority of three-way decisions in probabilistic rough set models
Information Sciences: an International Journal
A comparison of two kinds of definitions of rough approximations based on a similarity relation
Information Sciences: an International Journal
A vague-rough set approach for uncertain knowledge acquisition
Knowledge-Based Systems
Modeling rough granular computing based on approximation spaces
Information Sciences: an International Journal
Information Sciences: an International Journal
Combination entropy and combination granulation in incomplete information system
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
Multi knowledge based rough approximations and applications
Knowledge-Based Systems
Semantic Web search based on rough sets and Fuzzy Formal Concept Analysis
Knowledge-Based Systems
A rough set approach for estimating correlation measures in quality function deployment
Information Sciences: an International Journal
Local and global approximations for incomplete data
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Test-cost-sensitive attribute reduction
Information Sciences: an International Journal
Attribute reduction of data with error ranges and test costs
Information Sciences: an International Journal
A measurement theory view on the granularity of partitions
Information Sciences: an International Journal
Attribute selection based on a new conditional entropy for incomplete decision systems
Knowledge-Based Systems
Entropy measures and granularity measures for set-valued information systems
Information Sciences: an International Journal
Rough set approach to incomplete numerical data
Information Sciences: an International Journal
A novel method for attribute reduction of covering decision systems
Information Sciences: an International Journal
Information Sciences: an International Journal
Feature selection with test cost constraint
International Journal of Approximate Reasoning
Characteristic matrix of covering and its application to Boolean matrix decomposition
Information Sciences: an International Journal
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There are mainly two methodologies dealing with uncertainty measurement issue in rough set theory: pure rough set approach and information theory approach. Pure rough set approach is based on the concepts of accuracy, roughness and approximation accuracy proposed by Pawlak. Information theory approach is based on Shannon's entropy or its variants. Several authors have extended the information theory approach into incomplete information systems. However, there are few studies on extending the pure rough set approach to incomplete information systems. This paper focuses on constructing uncertainty measures in incomplete information systems by pure rough set approach. Three types of definitions of lower and upper approximations and corresponding uncertainty measurement concepts including accuracy, roughness and approximation accuracy are investigated. Theoretical analysis indicates that two of the three types can be used to evaluate the uncertainty in incomplete information systems. Experiments on incomplete real-life data sets have been conducted to test the two selected types (the first type and the third type) of uncertainty measures. Results show that the two types of uncertainty measures are effective.