Undersampling Instance Selection for Hybrid and Incomplete Imbalanced Data.
Oscar Camacho NietoCornelio Yáñez-MárquezYenny Villuendas-ReyPublished in: J. Univers. Comput. Sci. (2020)
Keyphrases
- imbalanced data
- instance selection
- class imbalance
- class imbalanced
- concept drift
- feature selection
- class distribution
- support vector machine
- nearest neighbor
- data reduction
- multiple instance learning
- multi class
- minority class
- classification accuracy
- sampling methods
- text classification
- cost sensitive
- supervised learning
- linear regression
- active learning
- cost sensitive learning
- semi supervised learning
- high dimensionality
- svm classifier
- ensemble methods
- random forest
- machine learning
- preprocessing
- decision boundary
- least squares
- text mining
- genetic programming
- knn
- labeled data
- data points
- pairwise
- training set
- neural network
- data analysis
- change detection