Why label when you can search?: alternatives to active learning for applying human resources to build classification models under extreme class imbalance.
Josh AttenbergFoster J. ProvostPublished in: KDD (2010)
Keyphrases
- class imbalance
- active learning
- classification models
- human resources
- feature selection
- imbalanced data
- training data
- cost sensitive learning
- class distribution
- imbalanced data classification
- imbalanced class distribution
- cost sensitive
- decision trees
- imbalanced datasets
- developing countries
- training set
- sampling methods
- training examples
- attribute selection
- learning process
- machine learning
- supervised learning
- feature set
- random sampling
- labeled data
- misclassification costs
- concept drift
- high dimensionality
- multi label
- class labels
- high dimensional data