Classification of Class-Imbalanced Data Sets
{"Image DocumentName": "Classification of Class-Imbalanced Data Sets", "Image ImageDescription": "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.", "Image XResolution": "37", "Image YResolution": "37", "Image ResolutionUnit": "Pixels/Centimeter", "Image Software": "SlidePlayer", "Image YCbCrPositioning": "Centered"}