Knowledge discovery in databases: an attribute-oriented rough set approach
Knowledge discovery in databases: an attribute-oriented rough set approach
Uncertainly measures of rough set prediction
Artificial Intelligence
Using Rough Sets with Heuristics for Feature Selection
Journal of Intelligent Information Systems
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
AI Communications - Special issue on Artificial intelligence advances in China
An introduction to variable and feature selection
The Journal of Machine Learning Research
Granular computing, rough entropy and object extraction
Pattern Recognition Letters
Feature selection based on rough sets and particle swarm optimization
Pattern Recognition Letters
Generalized rough sets, entropy, and image ambiguity measures
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
The information entropy of rough relational databases
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Feature selection with adjustable criteria
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Feature selection using rough entropy-based uncertainty measures in incomplete decision systems
Knowledge-Based Systems
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Rough set theory has been recognized to be one of the powerful tools for feature selection. The essence of rough set approach to feature selection is to find a minimal subset (or called reduct) of original features, which can predict the decision concepts as well as the original features. Since finding a minimal feature subset is a NP-hard problem, many heuristic algorithms have been proposed, which usually employ the feature significance in rough sets as heuristic information. Shannon's information theory provides us a feasible way to measure the information of data sets with entropy. Hence, many researchers have used information entropy to measure the feature significance in rough sets, and proposed different information entropy models in rough sets. In this paper, we present a new information entropy model, in which relative decision entropy is defined. Based on the relative decision entropy, we propose a new rough set algorithm to feature selection. To verify the efficiency of our algorithm, experiments are carried out on some UCI data sets. The results demonstrate that our algorithm can provide competitive solutions efficiently.