Software fault proneness prediction: a comparative study between bagging, boosting, and stacking ensemble and base learner methods.
Mohammed AkourIzzat AlsmadiIyad AlazzamPublished in: Int. J. Data Anal. Tech. Strateg. (2017)
Keyphrases
- ensemble methods
- ensemble learning
- base classifiers
- base learners
- prediction accuracy
- ensemble classifier
- benchmark datasets
- random forests
- machine learning methods
- majority voting
- decision tree ensembles
- ensemble classification
- classifier ensemble
- random forest
- ensemble members
- decision stumps
- multi class
- decision trees
- generalization ability
- weak classifiers
- training set
- subspace methods
- weak learners
- negative correlation learning
- multiple classifier systems
- random subspace
- gradient boosting
- machine learning
- individual classifiers
- regression problems
- linear regression
- fault diagnosis
- class labels
- genetic programming
- feature space
- feature selection