Japanese Credit Screening Data Set
Below are papers that cite this data set, with context shown.
Papers were automatically harvested and associated with this data set, in collaboration with Rexa.info.
Return to Japanese Credit Screening data set page.
Chris Drummond and Robert C. Holte. C4.5, Class Imbalance, and Cost Sensitivity: Why Under-Sampling beats Over-Sampling. Institute for Information Technology, National Research Council Canada.
Expected Cost 0.4 0.6 0.8 1.0 0.0 0.1 0.2 0.3 0.4 Å Å Å Å Å Å Å Å Å Å Å Å 0.0 Å 0.2 Figure 2. Sonar: Comparing Sampling Schemes Figure 3 shows the different sampling schemes for the Japanese credit data set. It has 690 instances, 307 positive and 383 negative, with 15 attributes, 6 real and 9 nominal. Again for under-sampling, the curve is reasonably smooth and this time remains completely within the