Email spam detection using bagging and boosting of machine learning classifiers.
Uma BhardwajPriti SharmaPublished in: Int. J. Adv. Intell. Paradigms (2023)
Keyphrases
- machine learning
- ensemble learning
- randomized trees
- ensemble methods
- decision trees
- machine learning methods
- ensemble classifier
- majority voting
- weak classifiers
- ensemble classification
- decision stumps
- feature selection
- machine learning algorithms
- rotation forest
- random forest
- meta learning
- base classifiers
- learning algorithm
- email spam
- boosted classifiers
- imbalanced data
- object detection
- learning machines
- multiple classifier systems
- discriminative classifiers
- random forests
- weighted voting
- accurate classifiers
- training data
- detection algorithm
- weak learners
- individual classifiers
- classifier ensemble
- support vector
- generalization ability
- base learners
- boosting algorithms
- feature set
- training set
- naive bayes
- supervised learning
- information extraction
- text mining
- model selection
- gradient boosting
- learning tasks
- natural language processing
- decision tree classifiers
- data mining
- multi class
- training samples
- combining multiple
- support vector machine
- spam filters
- prediction accuracy
- generalization error
- active learning
- text classification
- benchmark datasets