Current Slide
Small screen detected. You are viewing the mobile version of SlideWiki. If you wish to edit slides you will need to use a larger device.
Classification of Class-Imbalanced Data Sets
- Class-imbalance problem: Rare positive example but numerous negative ones, e.g., medical diagnosis, fraud, oil-spill, fault, etc.
- Traditional methods assume a balanced distribution of classes and equal error costs: not suitable for class-imbalanced data
- Typical methods for imbalance data in 2-class classification:
- Oversampling: re-sampling of data from positive class
- Under-sampling: randomly eliminate tuples from negative class
- Threshold-moving: moves the decision threshold, t, so that the rare class tuples are easier to classify, and hence, less chance of costly false negative errors
- Ensemble techniques: Ensemble multiple classifiers introduced above
- Still difficult for class imbalance problem on multiclass tasks
Speaker notes:
Content Tools
Tools
Sources (0)
Tags (0)
Comments (0)
History
Usage
Questions (0)
Playlists (0)
Quality
Sources
There are currently no sources for this slide.