{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/interpretable-sequence-classification-via-1","title":"Interpretable Sequence Classification via Discrete Optimization","arxiv_id":"2010.02819","date":"2020-10-06","proceeding":null,"authors":["Maayan Shvo","Andrew C. Li","Rodrigo Toro Icarte","Sheila A. McIlraith"],"abstract":"Sequence classification is the task of predicting a class label given a sequence of observations. In many applications such as healthcare monitoring or intrusion detection, early classification is crucial to prompt intervention. In this work, we learn sequence classifiers that favour early classification from an evolving observation trace. While many state-of-the-art sequence classifiers are neural networks, and in particular LSTMs, our classifiers take the form of finite state automata and are learned via discrete optimization. Our automata-based classifiers are interpretable---supporting explanation, counterfactual reasoning, and human-in-the-loop modification---and have strong empirical performance. Experiments over a suite of goal recognition and behaviour classification datasets show our learned automata-based classifiers to have comparable test performance to LSTM-based classifiers, with the added advantage of being interpretable.","url_abs":"https://arxiv.org/abs/2010.02819v1","url_pdf":"https://arxiv.org/pdf/2010.02819v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"interpretable-sequence-classification-via-1","repo_url":"https://github.com/andrewli77/DISC","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"counterfactual-reasoning","task_name":"Counterfactual Reasoning"},{"task_slug":"early-classification","task_name":"Early  Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"intrusion-detection","task_name":"Intrusion Detection"},{"task_slug":null,"task_name":"counterfactual"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2010.02819","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.02819"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/andrewli77/DISC","reach":null}],"summary":{"unverified":3},"by_repo_kind":{"official":{"samples":3,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"09430d740e9029cf","entry":"test_binary_accuracy","repo":"andrewli77/DISC","repo_kind":"official","path":"DFA_utils_tree_minerror.py","file_url":"https://github.com/andrewli77/DISC/blob/HEAD/DFA_utils_tree_minerror.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"09430d740e9029cf"}},{"code_sha256_prefix":"f38c6bd68d4e8eec","entry":"test_binary_convergence","repo":"andrewli77/DISC","repo_kind":"official","path":"DFA_utils_tree_minerror.py","file_url":"https://github.com/andrewli77/DISC/blob/HEAD/DFA_utils_tree_minerror.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"f38c6bd68d4e8eec"}},{"code_sha256_prefix":"6bf4524ebbf94df5","entry":"test_multilabel_convergence","repo":"andrewli77/DISC","repo_kind":"official","path":"DFA_utils_tree_minerror.py","file_url":"https://github.com/andrewli77/DISC/blob/HEAD/DFA_utils_tree_minerror.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"6bf4524ebbf94df5"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}