Variable precision rough set model
Journal of Computer and System Sciences
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Rough set methods in feature selection and recognition
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
Feature Selection Using Rough Sets Theory
ECML '93 Proceedings of the European Conference on Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
Consistency-based search in feature selection
Artificial Intelligence
Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information-preserving hybrid data reduction based on fuzzy-rough techniques
Pattern Recognition Letters
Measures for evaluating the decision performance of a decision table in rough set theory
Information Sciences: an International Journal
Mixed feature selection based on granulation and approximation
Knowledge-Based Systems
Attribute reduction in decision-theoretic rough set models
Information Sciences: an International Journal
A new measure of uncertainty based on knowledge granulation for rough sets
Information Sciences: an International Journal
Exploring the boundary region of tolerance rough sets for feature selection
Pattern Recognition
Discernibility matrix simplification for constructing attribute reducts
Information Sciences: an International Journal
FUN: Fast Discovery of Minimal Sets of Attributes Functionally Determining a Decision Attribute
Transactions on Rough Sets IX
Information Sciences: an International Journal
Information Processing and Management: an International Journal - Special issue: Formal methods for information retrieval
Positive approximation: An accelerator for attribute reduction in rough set theory
Artificial Intelligence
A vague-rough set approach for uncertain knowledge acquisition
Knowledge-Based Systems
Neighborhood systems-based rough sets in incomplete information system
Knowledge-Based Systems
Attributes Reduction Using Fuzzy Rough Sets
IEEE Transactions on Fuzzy Systems
A Comparative Study of Algebra Viewpoint and Information Viewpoint in Attribute Reduction
Fundamenta Informaticae
Fundamenta Informaticae
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Feature selection is an active area of research in pattern recognition, machine learning and artificial intelligence, which greatly improves the performance of forecasting or classification. In rough set theory, attribute reduction, as a special form of feature selection, aims to retain the discernability of the original attribute set. To solve this problem, many heuristic attribute reduction algorithms have been proposed in the literature. However, these methods are computationally time-consuming for large scale datasets. Recently, an accelerator was introduced by computing reducts on gradually reducing the size of the universe. Although the accelerator can considerably shorten the computational time, it remains a challenging issue. To further enhance the efficiency of these algorithms, we develop a new accelerator for attribute reduction, which simultaneously reduces the size of the universe and the number of attributes at each iteration of the process of reduction. Based on the new accelerator, several representative heuristic attribute reduction algorithms are accelerated. Experiments show that these accelerated algorithms can significantly reduce computational time while maintaining their results the same as before.