SMOTE-IPF: Addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering.
José A. SáezJulián LuengoJerzy StefanowskiFrancisco HerreraPublished in: Inf. Sci. (2015)
Keyphrases
- class imbalance
- imbalanced data sets
- imbalanced datasets
- imbalanced data
- class imbalanced
- binary classification problems
- pattern recognition
- classification accuracy
- class distribution
- high dimensionality
- minority class
- support vector machine svm
- feature extraction
- support vector
- image classification
- text classification
- decision trees
- feature space
- benchmark data sets
- cost sensitive
- cost sensitive learning
- support vector machine
- classification rules
- highly skewed
- concept learning
- data reduction
- feature vectors
- machine learning
- decision rules
- benchmark datasets