The rough sets theory and evidence theory
Fundamenta Informaticae
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
Rough set algorithms in classification problem
Rough set methods and applications
Unsupervised Feature Selection Using Feature Similarity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Logic Minimization Algorithms for VLSI Synthesis
Logic Minimization Algorithms for VLSI Synthesis
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Feature selection based on rough sets and particle swarm optimization
Pattern Recognition Letters
Information Sciences: an International Journal
An efficient bit-based feature selection method
Expert Systems with Applications: An International Journal
Discernibility matrix simplification for constructing attribute reducts
Information Sciences: an International Journal
A general definition of an attribute reduct
RSKT'07 Proceedings of the 2nd international conference on Rough sets and knowledge technology
Improved feature selection algorithm based on SVM and correlation
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Four matroidal structures of covering and their relationships with rough sets
International Journal of Approximate Reasoning
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The goal of feature selection (FS) is to find the minimal subset (MS) R of condition feature set C such that R has the same classification power as C and then reduce the dataset by discarding from it all features not contained in R. Usually one dataset may have a lot of MSs and finding all of them is known as an NP-hard problem. Therefore, when only one MS is required, some heuristic for finding only one or a small number of possible MSs is used. But in this case there is a risk that the best MSs would be overlooked. When the best solution of an FS task is required, the discernibility matrix (DM)-based approach, generating all MSs, is used. There are basically two factors that often cause to overflow the computer's memory due to which the DM-based FS programs fail. One of them is the largeness of sizes of discernibility functions (DFs) for large data sets; the other is the intractable space complexity of the conversion of a DF to disjunctive normal form (DNF). But usually most of the terms of DF and temporary results generated during DF to DNF conversion process are redundant ones. Therefore, usually the minimized DF (DF"m"i"n) and the final DNF is to be much simpler than the original DF and temporary results mentioned, respectively. Based on these facts, we developed a logic function-based feature selection method that derives DF"m"i"n from the truth table image of a dataset and converts it to DNF with preventing the occurrences of redundant terms. The proposed method requires no more amount of memory than that is required for constructing DF"m"i"n and final DNF separately. Due to this property, it can process most of datasets that can not be processed by DM-based programs.