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
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Reduction algorithms based on discernibility matrix: the ordered attributes method
Journal of Computer Science and Technology
Computers and Industrial Engineering
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
Approaches to knowledge reduction based on variable precision rough set model
Information Sciences—Informatics and Computer Science: An International Journal - Mining stream data
A comparison of rough set methods and representative inductive learning algorithms
Fundamenta Informaticae - Special issue on the 9th international conference on rough sets, fuzzy sets, data mining and granular computing (RSFDGrC 2003)
Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches
IEEE Transactions on Knowledge and Data Engineering
Feature selection based on rough sets and particle swarm optimization
Pattern Recognition Letters
Reducts and Constructs in Attribute Reduction
Fundamenta Informaticae - International Conference on Soft Computing and Distributed Processing (SCDP'2002)
Relations of attribute reduction between object and property oriented concept lattices
Knowledge-Based Systems
A filter model for feature subset selection based on genetic algorithm
Knowledge-Based Systems
Implement web learning environment based on data mining
Knowledge-Based Systems
Variable-precision dominance-based rough set approach and attribute reduction
International Journal of Approximate Reasoning
Approaches to attribute reduction in concept lattices induced by axialities
Knowledge-Based Systems
Dimensionality reduction and main component extraction of mass spectrometry cancer data
Knowledge-Based Systems
A novel business cycle surveillance system using the query logs of search engines
Knowledge-Based Systems
Large-margin feature selection for monotonic classification
Knowledge-Based Systems
Determinants of intangible assets value: The data mining approach
Knowledge-Based Systems
Graded rough set model based on two universes and its properties
Knowledge-Based Systems
On interval type-2 rough fuzzy sets
Knowledge-Based Systems
Bipolar fuzzy rough set model on two different universes and its application
Knowledge-Based Systems
Matroidal structure of rough sets and its characterization to attribute reduction
Knowledge-Based Systems
Feature selection using rough entropy-based uncertainty measures in incomplete decision systems
Knowledge-Based Systems
Information Sciences: an International Journal
Feature selection using dynamic weights for classification
Knowledge-Based Systems
Computing connected components of simple undirected graphs based on generalized rough sets
Knowledge-Based Systems
A novel feature selection method and its application
Journal of Intelligent Information Systems
Feature selection with test cost constraint
International Journal of Approximate Reasoning
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Feature selection is viewed as an important preprocessing step for pattern recognition, machine learning and data mining. Traditional hill-climbing search approaches to feature selection have difficulties to find optimal reducts. And the current stochastic search strategies, such as GA, ACO and PSO, provide a more robust solution but at the expense of increased computational effort. It is necessary to investigate fast and effective search algorithms. Rough set theory provides a mathematical tool to discover data dependencies and reduce the number of features contained in a dataset by purely structural methods. In this paper, we define a structure called power set tree (PS-tree), which is an order tree representing the power set, and each possible reduct is mapped to a node of the tree. Then, we present a rough set approach to feature selection based on PS-tree. Two kinds of pruning rules for PS-tree are given. And two novel feature selection algorithms based on PS-tree are also given. Experiment results demonstrate that our algorithms are effective and efficient.