DualPOS: A Semi-supervised Attribute Selection Approach for Symbolic Data Based on Rough Set Theory.
Jianhua DaiHuifeng HanHu HuQinghua HuJinghong ZhangWentao WangPublished in: WAIM (2) (2016)
Keyphrases
- rough set theory
- attribute selection
- symbolic data
- semi supervised
- rough sets
- semi supervised learning
- numerical data
- interval data
- complex data
- decision tree induction
- attribute reduction
- decision trees
- data sets
- labeled data
- decision rules
- data analysis
- unlabeled data
- decision table
- principal component analysis
- feature space
- information gain
- classification models
- knowledge discovery
- unsupervised learning
- data reduction
- supervised learning
- data mining
- active learning
- naive bayes
- image processing
- feature selection
- neural network
- machine learning
- training data
- concept lattice
- dimensionality reduction