Fault diagnosis using Rough Sets Theory
Computers in Industry
Using Rough Sets with Heuristics for Feature Selection
Journal of Intelligent Information Systems
Feature Extraction, Construction and Selection: A Data Mining Perspective
Feature Extraction, Construction and Selection: A Data Mining Perspective
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Rough set methods in feature selection and recognition
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
Intelligent fault inference for rotating flexible rotors using Bayesian belief network
Expert Systems with Applications: An International Journal
Rough sets in the Soft Computing environment
Information Sciences: an International Journal
Combining rough set and case based reasoning for process conditions selection in camshaft grinding
Journal of Intelligent Manufacturing
| Hi-index | 12.05 |
Rough set theory (RS) has been a topic of general interest in the field of knowledge discovery and pattern recognition. Machine learning algorithms are known to degrade in performance when faced with many features (sometimes attributes) that are not necessary for rule discovery. Many methods for selecting a subset of features have been proposed. However, only one method cannot handle the complex system with many attributes or features, so a hybrid mechanism is proposed based on rough set integrating artificial neural network (Rough-ANN) for feature selection in pattern recognition. RS-based attributes reduction as the preprocessor can decrease the inputs of the NN and improve the speed of training. So the sensitivity of rough set to noise can be avoided and the system's robustness is to be improved. A RS-based heuristic algorithm is proposed for feature selection. The approach can select an optimal subset of features quickly and effectively from a large database with a lot of features. Moreover, the validity of the proposed hybrid recognizer and solution is verified by the application of practical experiments and fault diagnosis in industrial process.