{"paper":{"title":"Popular Ensemble Methods: An Empirical Study","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"D. Opitz, R. Maclin","submitted_at":"2011-06-01T16:41:44Z","abstract_excerpt":"An ensemble consists of a set of individually trained    classifiers (such as neural networks or decision trees) whose    predictions are combined when classifying novel instances.  Previous    research has shown that an ensemble is often more accurate than any of    the single classifiers in the ensemble.  Bagging (Breiman, 1996c) and    Boosting (Freund and Shapire, 1996; Shapire, 1990) are two relatively    new but popular methods for producing ensembles.  In this paper we    evaluate these methods on 23 data sets using both neural networks and    decision trees as our classification algori"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1106.0257","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"}