Investigating the Variation of Ensemble Size on Bagging-Based Classifier Performance in Imbalanced Bioinformatics Datasets.
Alireza FazelpourTaghi M. KhoshgoftaarDavid J. DittmanAmri NapolitanoPublished in: IRI (2016)
Keyphrases
- imbalanced data
- ensemble methods
- class imbalance
- class distribution
- imbalanced datasets
- ensemble learning
- base classifiers
- sampling methods
- random forest
- majority voting
- benchmark datasets
- minority class
- machine learning
- binary classification problems
- classifier ensemble
- random forests
- ensemble members
- support vector machine
- linear regression
- cost sensitive
- ensemble selection
- ensemble classifier
- ensemble classification
- training set
- decision tree classifiers
- binary classification
- class label noise
- generalization ability
- decision trees
- feature selection
- prediction accuracy
- active learning
- training data
- data sets
- classification models
- machine learning methods
- test data
- test set
- text mining
- accurate classifiers
- data mining
- neural network
- randomized trees