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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.

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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 0.0 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


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