Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Rough computational methods for information systems
Artificial Intelligence
Rough set approach to incomplete information systems
Information Sciences: an International Journal
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
The algorithm on knowledge reduction in incomplete information systems
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Benchmarking Attribute Selection Techniques for Discrete Class Data Mining
IEEE Transactions on Knowledge and Data Engineering
Consistency-based search in feature selection
Artificial Intelligence
RRIA: a rough set and rule tree based incremental knowledge acquisition algorithm
Fundamenta Informaticae - Special issue on the 9th international conference on rough sets, fuzzy sets, data mining and granular computing (RSFDGrC 2003)
"Missing Is Useful': Missing Values in Cost-Sensitive Decision Trees
IEEE Transactions on Knowledge and Data Engineering
Probabilistic rough set approximations
International Journal of Approximate Reasoning
Information Sciences: an International Journal
Are more features better? a response to attributes reduction using fuzzy rough sets
IEEE Transactions on Fuzzy Systems
Reduction about approximation spaces of covering generalized rough sets
International Journal of Approximate Reasoning
Positive approximation: An accelerator for attribute reduction in rough set theory
Artificial Intelligence
Approximation reduction in inconsistent incomplete decision tables
Knowledge-Based Systems
International Journal of Intelligent Systems
The incremental method for fast computing the rough fuzzy approximations
Data & Knowledge Engineering
Hybrid approaches to attribute reduction based on indiscernibility and discernibility relation
International Journal of Approximate Reasoning
Incremental attribute reduction based on elementary sets
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
International Journal of Approximate Reasoning
Incomplete Multigranulation Rough Set
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Feature Selection for Monotonic Classification
IEEE Transactions on Fuzzy Systems
Feature selection using rough entropy-based uncertainty measures in incomplete decision systems
Knowledge-Based Systems
International Journal of Approximate Reasoning
Attribute reduction for dynamic data sets
Applied Soft Computing
Attribute reduction: A dimension incremental strategy
Knowledge-Based Systems
IEEE Transactions on Knowledge and Data Engineering
Generalized probabilistic approximations of incomplete data
International Journal of Approximate Reasoning
Multigranulation decision-theoretic rough sets
International Journal of Approximate Reasoning
Feature selection with test cost constraint
International Journal of Approximate Reasoning
A Group Incremental Approach to Feature Selection Applying Rough Set Technique
IEEE Transactions on Knowledge and Data Engineering
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In rough set theory, attribute reduction is a challenging problem in the applications in which data with numbers of attributes available. Moreover, due to dynamic characteristics of data collection in decision systems, attribute reduction will change dynamically as attribute set in decision systems varies over time. How to carry out updating attribute reduction by utilizing previous information is an important task that can help to improve the efficiency of knowledge discovery. In view of that attribute reduction algorithms in incomplete decision systems with the variation of attribute set have not yet been discussed so far. This paper focuses on positive region-based attribute reduction algorithm to solve the attribute reduction problem efficiently in the incomplete decision systems with dynamically varying attribute set. We first introduce an incremental manner to calculate the new positive region and tolerance classes. Consequently, based on the calculated positive region and tolerance classes, the corresponding attribute reduction algorithms on how to compute new attribute reduct are put forward respectively when an attribute set is added into and deleted from the incomplete decision systems. Finally, numerical experiments conducted on different data sets from UCI validate the effectiveness and efficiency of the proposed algorithms in incomplete decision systems with the variation of attribute set.