{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:XN7A7FFY5ECGEVAKRXN6YVEMEP","short_pith_number":"pith:XN7A7FFY","schema_version":"1.0","canonical_sha256":"bb7e0f94b8e90462540a8ddbec548c23fa2f34f47c878ffe3f31a66be3181418","source":{"kind":"arxiv","id":"2208.11852","version":1},"attestation_state":"computed","paper":{"title":"An Empirical Analysis of the Efficacy of Different Sampling Techniques for Imbalanced Classification","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Asif Newaz, Farhan Shahriyar Haq, Shahriar Hassan","submitted_at":"2022-08-25T03:45:34Z","abstract_excerpt":"Learning from imbalanced data is a challenging task. Standard classification algorithms tend to perform poorly when trained on imbalanced data. Some special strategies need to be adopted, either by modifying the data distribution or by redesigning the underlying classification algorithm to achieve desirable performance. The prevalence of imbalance in real-world datasets has led to the creation of a multitude of strategies for the class imbalance issue. However, not all the strategies are useful or provide good performance in different imbalance scenarios. There are numerous approaches to deali"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2208.11852","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2022-08-25T03:45:34Z","cross_cats_sorted":[],"title_canon_sha256":"2a88b9ba2bc5301a8e7116c3bd1e4735a148247ac2d6e75fc7c16014fdbd29af","abstract_canon_sha256":"66aeacef10f28ba6a66e9dfc49339a493e0307ee4ac001b273aba2a16b185f88"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:51:26.811855Z","signature_b64":"OskSB34dSQa+/IhCMtMG1TYMobIHRyn34yK5jiKnazj7Skb5UTQ92kj1OKm8soWCx2skgAH/2HuIXUxQprvODw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bb7e0f94b8e90462540a8ddbec548c23fa2f34f47c878ffe3f31a66be3181418","last_reissued_at":"2026-07-05T04:51:26.811433Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:51:26.811433Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Empirical Analysis of the Efficacy of Different Sampling Techniques for Imbalanced Classification","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Asif Newaz, Farhan Shahriyar Haq, Shahriar Hassan","submitted_at":"2022-08-25T03:45:34Z","abstract_excerpt":"Learning from imbalanced data is a challenging task. Standard classification algorithms tend to perform poorly when trained on imbalanced data. Some special strategies need to be adopted, either by modifying the data distribution or by redesigning the underlying classification algorithm to achieve desirable performance. The prevalence of imbalance in real-world datasets has led to the creation of a multitude of strategies for the class imbalance issue. However, not all the strategies are useful or provide good performance in different imbalance scenarios. There are numerous approaches to deali"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.11852","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2208.11852/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2208.11852","created_at":"2026-07-05T04:51:26.811496+00:00"},{"alias_kind":"arxiv_version","alias_value":"2208.11852v1","created_at":"2026-07-05T04:51:26.811496+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.11852","created_at":"2026-07-05T04:51:26.811496+00:00"},{"alias_kind":"pith_short_12","alias_value":"XN7A7FFY5ECG","created_at":"2026-07-05T04:51:26.811496+00:00"},{"alias_kind":"pith_short_16","alias_value":"XN7A7FFY5ECGEVAK","created_at":"2026-07-05T04:51:26.811496+00:00"},{"alias_kind":"pith_short_8","alias_value":"XN7A7FFY","created_at":"2026-07-05T04:51:26.811496+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XN7A7FFY5ECGEVAKRXN6YVEMEP","json":"https://pith.science/pith/XN7A7FFY5ECGEVAKRXN6YVEMEP.json","graph_json":"https://pith.science/api/pith-number/XN7A7FFY5ECGEVAKRXN6YVEMEP/graph.json","events_json":"https://pith.science/api/pith-number/XN7A7FFY5ECGEVAKRXN6YVEMEP/events.json","paper":"https://pith.science/paper/XN7A7FFY"},"agent_actions":{"view_html":"https://pith.science/pith/XN7A7FFY5ECGEVAKRXN6YVEMEP","download_json":"https://pith.science/pith/XN7A7FFY5ECGEVAKRXN6YVEMEP.json","view_paper":"https://pith.science/paper/XN7A7FFY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2208.11852&json=true","fetch_graph":"https://pith.science/api/pith-number/XN7A7FFY5ECGEVAKRXN6YVEMEP/graph.json","fetch_events":"https://pith.science/api/pith-number/XN7A7FFY5ECGEVAKRXN6YVEMEP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XN7A7FFY5ECGEVAKRXN6YVEMEP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XN7A7FFY5ECGEVAKRXN6YVEMEP/action/storage_attestation","attest_author":"https://pith.science/pith/XN7A7FFY5ECGEVAKRXN6YVEMEP/action/author_attestation","sign_citation":"https://pith.science/pith/XN7A7FFY5ECGEVAKRXN6YVEMEP/action/citation_signature","submit_replication":"https://pith.science/pith/XN7A7FFY5ECGEVAKRXN6YVEMEP/action/replication_record"}},"created_at":"2026-07-05T04:51:26.811496+00:00","updated_at":"2026-07-05T04:51:26.811496+00:00"}