Boosting Accuracy of Classical Machine Learning Antispam Classifiers in Real Scenarios by Applying Rough Set Theory.
Noemí Pérez-DíazDavid Ruano-OrdásFlorentino Fdez-RiverolaJosé Ramon MéndezPublished in: Sci. Program. (2016)
Keyphrases
- rough set theory
- rough sets
- machine learning
- improving classification accuracy
- multiple classifier systems
- feature selection
- decision table
- feature reduction
- machine learning methods
- attribute reduction
- decision trees
- decision rules
- rule generation
- knowledge discovery
- variable precision rough set model
- decision stumps
- tool for data mining
- training data
- data analysis
- granular computing
- data reduction
- ensemble learning
- support vector machine
- classification accuracy
- computer vision
- learning algorithm
- rule extraction
- fold cross validation
- real world
- data mining
- pattern recognition
- ensemble methods
- support vector
- knowledge reduction
- neural network
- rough fuzzy
- rough sets theory
- equivalence relation
- information entropy
- rule induction
- weak classifiers
- computational intelligence
- attribute set
- artificial intelligence
- approximation spaces
- semi supervised
- fuzzy logic
- discernibility matrix
- supervised learning
- rough approximations
- text classification
- naive bayes
- fuzzy rough sets
- concept lattice