{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:6YANZ7RDNFEFPMDJ53EVRCOFBC","short_pith_number":"pith:6YANZ7RD","canonical_record":{"source":{"id":"1804.05589","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-04-16T10:13:54Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c7c8347a0af30e27cf76048e70b715d393ddaeb0b2e2dc927931f2a7a15ec3e0","abstract_canon_sha256":"d21abde2cfe29b7038ed718d3b6f15d13a5dedc0b74df20e2d83d089e2dfcb9d"},"schema_version":"1.0"},"canonical_sha256":"f600dcfe23694857b069eec95889c508905631beb0e55c85438fb5639863e25e","source":{"kind":"arxiv","id":"1804.05589","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1804.05589","created_at":"2026-05-18T00:18:27Z"},{"alias_kind":"arxiv_version","alias_value":"1804.05589v1","created_at":"2026-05-18T00:18:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.05589","created_at":"2026-05-18T00:18:27Z"},{"alias_kind":"pith_short_12","alias_value":"6YANZ7RDNFEF","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_16","alias_value":"6YANZ7RDNFEFPMDJ","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_8","alias_value":"6YANZ7RD","created_at":"2026-05-18T12:32:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:6YANZ7RDNFEFPMDJ53EVRCOFBC","target":"record","payload":{"canonical_record":{"source":{"id":"1804.05589","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-04-16T10:13:54Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c7c8347a0af30e27cf76048e70b715d393ddaeb0b2e2dc927931f2a7a15ec3e0","abstract_canon_sha256":"d21abde2cfe29b7038ed718d3b6f15d13a5dedc0b74df20e2d83d089e2dfcb9d"},"schema_version":"1.0"},"canonical_sha256":"f600dcfe23694857b069eec95889c508905631beb0e55c85438fb5639863e25e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:18:27.790335Z","signature_b64":"KDlF/Gr4iVCx/15D7yk3/J7T5bFYoRq0wZTiGAAo0Ff8+P3T1Jpi54wCH/qFFtDBf7sEi6IFYSxt0vtfvxLPDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f600dcfe23694857b069eec95889c508905631beb0e55c85438fb5639863e25e","last_reissued_at":"2026-05-18T00:18:27.789961Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:18:27.789961Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1804.05589","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:18:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iBUZc0ZeEgTSYUQlQQkYfWJOae3Q2sNS0Q7I2soHfMT8/btb3UIPcpxJbit/NzQfTIEwBmCbon5iqPDuqkO7CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-26T03:19:42.260526Z"},"content_sha256":"7038ab1c5f0de0fc0ecef7f2e70a4b8e75249e0275403508b7659cc7f7ca5c85","schema_version":"1.0","event_id":"sha256:7038ab1c5f0de0fc0ecef7f2e70a4b8e75249e0275403508b7659cc7f7ca5c85"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:6YANZ7RDNFEFPMDJ53EVRCOFBC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SPSA-FSR: Simultaneous Perturbation Stochastic Approximation for Feature Selection and Ranking","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Alev Taskin Gumus, Babak Abbasi, Niranjan Adhikari, Vural Aksakalli, Yong Kai Wong, Zeren D. Yenice","submitted_at":"2018-04-16T10:13:54Z","abstract_excerpt":"This manuscript presents the following: (1) an improved version of the Binary Simultaneous Perturbation Stochastic Approximation (SPSA) Method for feature selection in machine learning (Aksakalli and Malekipirbazari, Pattern Recognition Letters, Vol. 75, 2016) based on non-monotone iteration gains computed via the Barzilai and Borwein (BB) method, (2) its adaptation for feature ranking, and (3) comparison against popular methods on public benchmark datasets. The improved method, which we call SPSA-FSR, dramatically reduces the number of iterations required for convergence without impacting sol"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.05589","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:18:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rGQMrvN6FYbTruYAytn4b9o5eOVuQAutdv1LyjZI0eXYKXODZdILP7WG2O+4ZfM2TfNQS2lKruVMbHOLkJ7pAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-26T03:19:42.261118Z"},"content_sha256":"2722730eb1a905efb225d56885fbf652dac33d6fba959c38fe8f947ab9cef68b","schema_version":"1.0","event_id":"sha256:2722730eb1a905efb225d56885fbf652dac33d6fba959c38fe8f947ab9cef68b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6YANZ7RDNFEFPMDJ53EVRCOFBC/bundle.json","state_url":"https://pith.science/pith/6YANZ7RDNFEFPMDJ53EVRCOFBC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6YANZ7RDNFEFPMDJ53EVRCOFBC/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-26T03:19:42Z","links":{"resolver":"https://pith.science/pith/6YANZ7RDNFEFPMDJ53EVRCOFBC","bundle":"https://pith.science/pith/6YANZ7RDNFEFPMDJ53EVRCOFBC/bundle.json","state":"https://pith.science/pith/6YANZ7RDNFEFPMDJ53EVRCOFBC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6YANZ7RDNFEFPMDJ53EVRCOFBC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:6YANZ7RDNFEFPMDJ53EVRCOFBC","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":"d21abde2cfe29b7038ed718d3b6f15d13a5dedc0b74df20e2d83d089e2dfcb9d","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-04-16T10:13:54Z","title_canon_sha256":"c7c8347a0af30e27cf76048e70b715d393ddaeb0b2e2dc927931f2a7a15ec3e0"},"schema_version":"1.0","source":{"id":"1804.05589","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1804.05589","created_at":"2026-05-18T00:18:27Z"},{"alias_kind":"arxiv_version","alias_value":"1804.05589v1","created_at":"2026-05-18T00:18:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.05589","created_at":"2026-05-18T00:18:27Z"},{"alias_kind":"pith_short_12","alias_value":"6YANZ7RDNFEF","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_16","alias_value":"6YANZ7RDNFEFPMDJ","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_8","alias_value":"6YANZ7RD","created_at":"2026-05-18T12:32:11Z"}],"graph_snapshots":[{"event_id":"sha256:2722730eb1a905efb225d56885fbf652dac33d6fba959c38fe8f947ab9cef68b","target":"graph","created_at":"2026-05-18T00:18:27Z","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"},"paper":{"abstract_excerpt":"This manuscript presents the following: (1) an improved version of the Binary Simultaneous Perturbation Stochastic Approximation (SPSA) Method for feature selection in machine learning (Aksakalli and Malekipirbazari, Pattern Recognition Letters, Vol. 75, 2016) based on non-monotone iteration gains computed via the Barzilai and Borwein (BB) method, (2) its adaptation for feature ranking, and (3) comparison against popular methods on public benchmark datasets. The improved method, which we call SPSA-FSR, dramatically reduces the number of iterations required for convergence without impacting sol","authors_text":"Alev Taskin Gumus, Babak Abbasi, Niranjan Adhikari, Vural Aksakalli, Yong Kai Wong, Zeren D. Yenice","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-04-16T10:13:54Z","title":"SPSA-FSR: Simultaneous Perturbation Stochastic Approximation for Feature Selection and Ranking"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.05589","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":"sha256:7038ab1c5f0de0fc0ecef7f2e70a4b8e75249e0275403508b7659cc7f7ca5c85","target":"record","created_at":"2026-05-18T00:18:27Z","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":"d21abde2cfe29b7038ed718d3b6f15d13a5dedc0b74df20e2d83d089e2dfcb9d","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-04-16T10:13:54Z","title_canon_sha256":"c7c8347a0af30e27cf76048e70b715d393ddaeb0b2e2dc927931f2a7a15ec3e0"},"schema_version":"1.0","source":{"id":"1804.05589","kind":"arxiv","version":1}},"canonical_sha256":"f600dcfe23694857b069eec95889c508905631beb0e55c85438fb5639863e25e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f600dcfe23694857b069eec95889c508905631beb0e55c85438fb5639863e25e","first_computed_at":"2026-05-18T00:18:27.789961Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:18:27.789961Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"KDlF/Gr4iVCx/15D7yk3/J7T5bFYoRq0wZTiGAAo0Ff8+P3T1Jpi54wCH/qFFtDBf7sEi6IFYSxt0vtfvxLPDg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:18:27.790335Z","signed_message":"canonical_sha256_bytes"},"source_id":"1804.05589","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7038ab1c5f0de0fc0ecef7f2e70a4b8e75249e0275403508b7659cc7f7ca5c85","sha256:2722730eb1a905efb225d56885fbf652dac33d6fba959c38fe8f947ab9cef68b"],"state_sha256":"be6d7ab3e30cd431d88ec8af3a9196023806117392d6b3aa57ae343391f76ccf"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yiPQtxGAWcKl1Z3haNe88pqvXtzPKqAy4iztLp0v2EeA1w+wHrrkxVi7FmE4N2hE1/eFvue2slBdt8FnJeUVAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-26T03:19:42.263836Z","bundle_sha256":"28b4024d1f8961b02ab576bcc307a28d2583b942d8646d692bb2e4c8ff1a323f"}}