Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Rough computational methods for information systems
Artificial Intelligence
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
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
Rough set methods in feature selection and recognition
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
Using Rough Sets with Heuristics for Feature Selection
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches
IEEE Transactions on Knowledge and Data Engineering
Rough set feature selection methods for case-based categorization of text documents
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Feature selection by ordered rough set based feature weighting
DEXA'05 Proceedings of the 16th international conference on Database and Expert Systems Applications
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
Finding rough set reducts with SAT
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Rough set feature selection algorithms for textual case-based classification
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
Feature selection for medical dataset using rough set theory
CEA'09 Proceedings of the 3rd WSEAS international conference on Computer engineering and applications
A novel algorithm based on conditional entropy established by clustering for feature selection
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
A novel attribute reduction algorithm of decomposition based on rough sets
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 4
Two novel feature selection methods based on decomposition and composition
Expert Systems with Applications: An International Journal
A novel approach to improving C-Tree for feature selection
Applied Soft Computing
Evolutionary tolerance-based gene selection in gene expression data
Transactions on rough sets XIV
Finding rough and fuzzy-rough set reducts with SAT
Information Sciences: an International Journal
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Rough set approach is one of effective feature selection methods that can preserve the meaning of the features. The essence of feature selection based on rough set approach is to find a subset of the original features (attributes) using rough set theory. So far, many feature selection (also called feature reduction) methods based on rough set have been proposed, where numerous experimental results have demonstrated that these methods based on discernibility matrix are concise and efficient, but have much higher space complexity. In order to reduce the storage space of the existing feature selection methods based on discernibility matrix, in this paper, we introduce a novel condensing tree structure (C-Tree), which is an extended order-tree, every non-empty element of a discernibility matrix is mapped to one path in the C-Tree and a lot of non-empty elements may share the same path or prefix, so the C-Tree has much lower space complexity as compared to discernibility matrix. Moreover, our feature selection algorithms employ the C-Tree structure and incorporate some heuristic strategies, hence efficiently reduce both space and computational complexities. Algorithms of this paper are experimented using some standard datasets and synthetic datasets for testing both time and space complexities. Experimental results show that the algorithms of this paper can efficiently reduce the cost of storage and be computationally inexpensive when compared to the existing algorithms based on discernibility matrix for feature reduction.