Attribute clustering using rough set theory for feature selection in fault severity classification of rotating machinery.
Fannia PachecoMariela CerradaRené-Vinicio SánchezDiego CabreraChuan LiJosé Valente de OliveiraPublished in: Expert Syst. Appl. (2017)
Keyphrases
- rough set theory
- feature selection
- rough sets
- feature reduction
- decision rules
- attribute reduction
- rotating machinery
- knowledge reduction
- decision table
- rule generation
- rough fuzzy
- rule extraction
- condition attributes
- fault detection
- high dimensionality
- classification accuracy
- fault diagnosis
- discernibility matrix
- data reduction
- attribute set
- rule induction
- information entropy
- rule sets
- attribute reduction algorithm
- support vector
- machine learning
- support vector machine
- variable precision rough set model
- soft set
- clustering algorithm
- text classification
- feature set
- feature space
- rough sets theory
- classification models
- decision trees
- pattern recognition
- granular computing
- knowledge discovery
- feature ranking
- high dimensional data
- fuzzy sets
- dimensionality reduction
- feature extraction
- fuzzy rough sets
- real time
- tool for data mining
- equivalence relation
- feature subset
- microarray
- text categorization
- data analysis
- databases
- categorical data
- feature selection algorithms
- model selection
- knowledge base