Fuzzy-synthetic minority oversampling technique: Oversampling based on fuzzy set theory for Android malware detection in imbalanced datasets.
Yanping XuChunhua WuKangfeng ZhengXinxin NiuYixian YangPublished in: Int. J. Distributed Sens. Networks (2017)
Keyphrases
- fuzzy set theory
- malware detection
- minority class
- imbalanced datasets
- class imbalance
- majority class
- fuzzy sets
- class distribution
- fuzzy logic
- cost sensitive learning
- imbalanced data
- classification error
- fuzzy numbers
- nearest neighbour
- sampling methods
- rough set theory
- anomaly detection
- decision boundary
- imbalanced data sets
- original data
- support vector machine
- cost sensitive
- training data
- misclassification costs
- training set
- active learning
- membership functions
- ensemble learning
- rough sets
- training dataset
- data sets
- genetic algorithm
- feature selection
- pattern recognition
- expert systems
- artificial neural networks
- computational intelligence
- support vector
- pattern classification
- decision trees
- decision making
- dimensionality reduction
- test data
- model selection