{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:OFJYR7LMFXXXITAMBMT7DHY4XG","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"b99151b11ae01fdbd524310981b9c9f5437a4f4360be2a547a90ec923aafbb73","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2020-09-01T05:12:22Z","title_canon_sha256":"bf64878dffb8ae98122e22ff93b9f407a534108849d187d49cfd1547104ff546"},"schema_version":"1.0","source":{"id":"2009.00237","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2009.00237","created_at":"2026-07-05T01:32:16Z"},{"alias_kind":"arxiv_version","alias_value":"2009.00237v1","created_at":"2026-07-05T01:32:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2009.00237","created_at":"2026-07-05T01:32:16Z"},{"alias_kind":"pith_short_12","alias_value":"OFJYR7LMFXXX","created_at":"2026-07-05T01:32:16Z"},{"alias_kind":"pith_short_16","alias_value":"OFJYR7LMFXXXITAM","created_at":"2026-07-05T01:32:16Z"},{"alias_kind":"pith_short_8","alias_value":"OFJYR7LM","created_at":"2026-07-05T01:32:16Z"}],"graph_snapshots":[{"event_id":"sha256:e775aeda113494ee74f5e9dd80fd04b9322656bee8b4225ab7e7f7a2f9551a1f","target":"graph","created_at":"2026-07-05T01:32:16Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2009.00237/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"A general fuzzy min-max (GFMM) neural network is one of the efficient neuro-fuzzy systems for classification problems. However, a disadvantage of most of the current learning algorithms for GFMM is that they can handle effectively numerical valued features only. Therefore, this paper provides some potential approaches to adapting GFMM learning algorithms for classification problems with mixed-type or only categorical features as they are very common in practical applications and often carry very useful information. We will compare and assess three main methods of handling datasets with mixed f","authors_text":"Bogdan Gabrys, Thanh Tung Khuat","cross_cats":["stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2020-09-01T05:12:22Z","title":"An in-depth comparison of methods handling mixed-attribute data for general fuzzy min-max neural network"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2009.00237","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:450459c62941d3512fcd6a47898d5d23e6bc4f823d63c15b5cc17723bb7b8bac","target":"record","created_at":"2026-07-05T01:32:16Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"b99151b11ae01fdbd524310981b9c9f5437a4f4360be2a547a90ec923aafbb73","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2020-09-01T05:12:22Z","title_canon_sha256":"bf64878dffb8ae98122e22ff93b9f407a534108849d187d49cfd1547104ff546"},"schema_version":"1.0","source":{"id":"2009.00237","kind":"arxiv","version":1}},"canonical_sha256":"715388fd6c2def744c0c0b27f19f1cb9aa7265f13f3bb18b40e379ad75f4830b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"715388fd6c2def744c0c0b27f19f1cb9aa7265f13f3bb18b40e379ad75f4830b","first_computed_at":"2026-07-05T01:32:16.690969Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:32:16.690969Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bZobIKHWSdaGTRFuO3xFRqg75jlK4JCFtvlylv1z5brfR8HPOvf1fA/houtS1rFlBjKFhw05qzLvYKL+UXtMDg==","signature_status":"signed_v1","signed_at":"2026-07-05T01:32:16.691363Z","signed_message":"canonical_sha256_bytes"},"source_id":"2009.00237","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:450459c62941d3512fcd6a47898d5d23e6bc4f823d63c15b5cc17723bb7b8bac","sha256:e775aeda113494ee74f5e9dd80fd04b9322656bee8b4225ab7e7f7a2f9551a1f"],"state_sha256":"26cb799e84f627cb3dc1c27b08c056f80d12bfd6954cdb380fe7dfa7741b17db"}