{"paper":{"title":"SMOTE: Synthetic Minority Over-sampling Technique","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"K. W. Bowyer, L. O. Hall, N. V. Chawla, W. P. Kegelmeyer","submitted_at":"2011-06-09T13:53:42Z","abstract_excerpt":"An approach to the construction of classifiers from    imbalanced datasets is described. A dataset is imbalanced if the    classification categories are not approximately equally    represented. Often real-world data sets are predominately composed of    \"normal\" examples with only a small percentage of \"abnormal\" or    \"interesting\" examples. It is also the case that the cost of    misclassifying an abnormal (interesting) example as a normal example    is often much higher than the cost of the reverse    error. Under-sampling of the majority (normal) class has been proposed    as a good means"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1106.1813","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}