An intrusion detection scheme based on the ensemble of discriminant classifiers.
Bhoopesh Singh BhatiChandra Shekhar RaiBalamurugan BalusamyFadi Al-TurjmanPublished in: Comput. Electr. Eng. (2020)
Keyphrases
- detection scheme
- ensemble learning
- ensemble classifier
- classifier ensemble
- multiple classifiers
- ensemble pruning
- training data
- majority voting
- linear discriminant
- training set
- one class support vector machines
- individual classifiers
- final classification
- intrusion detection
- majority vote
- weighted voting
- combining classifiers
- ensemble methods
- feature selection
- anomaly detection
- ensemble classification
- discriminant analysis
- decision trees
- randomized trees
- weak classifiers
- diversity measures
- support vector
- accurate classifiers
- random forests
- neural network
- mining concept drifting data streams
- concept drifting data streams
- weak learners
- decision tree classifiers
- pruning algorithm
- machine learning algorithms
- ensemble members
- training samples
- random forest
- multiple classifier systems
- feature set
- rule induction algorithm
- class label noise
- classification algorithm
- machine learning methods
- imbalanced data
- intrusion detection system
- base classifiers
- bias variance decomposition
- trained classifiers
- feature ranking
- publicly available data sets
- classifier combination
- learning algorithm
- binary classification problems
- fusion methods
- binary classifiers
- concept drift
- principal component analysis
- feature extraction
- machine learning