{"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/scalable-rule-based-representation-learning","title":"Scalable Rule-Based Representation Learning for Interpretable Classification","arxiv_id":"2109.15103","date":"2021-09-30","proceeding":"NeurIPS 2021 12","authors":["Zhuo Wang","Wei zhang","Ning Liu","Jianyong Wang"],"abstract":"Rule-based models, e.g., decision trees, are widely used in scenarios demanding high model interpretability for their transparent inner structures and good model expressivity. However, rule-based models are hard to optimize, especially on large data sets, due to their discrete parameters and structures. Ensemble methods and fuzzy/soft rules are commonly used to improve performance, but they sacrifice the model interpretability. To obtain both good scalability and interpretability, we propose a new classifier, named Rule-based Representation Learner (RRL), that automatically learns interpretable non-fuzzy rules for data representation and classification. To train the non-differentiable RRL effectively, we project it to a continuous space and propose a novel training method, called Gradient Grafting, that can directly optimize the discrete model using gradient descent. An improved design of logical activation functions is also devised to increase the scalability of RRL and enable it to discretize the continuous features end-to-end. Exhaustive experiments on nine small and four large data sets show that RRL outperforms the competitive interpretable approaches and can be easily adjusted to obtain a trade-off between classification accuracy and model complexity for different scenarios. Our code is available at: https://github.com/12wang3/rrl.","url_abs":"https://arxiv.org/abs/2109.15103v1","url_pdf":"https://arxiv.org/pdf/2109.15103v1.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":"scalable-rule-based-representation-learning","repo_url":"https://github.com/12wang3/rrl","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"scalable-rule-based-representation-learning","repo_url":"https://github.com/12wang3/mllp","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2109.15103","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.15103"}},"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":"deterministic:regex_extraction","url":"https://github.com/12wang3/rrl","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/12wang3/mllp","reach":null}],"summary":{"ran":6},"by_repo_kind":{"listed":{"samples":6,"ran":6,"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":"1278025082c02427","entry":"ConjunctionLayer","repo":"12wang3/mllp","repo_kind":"listed","path":"mllp/models.py","file_url":"https://github.com/12wang3/mllp/blob/HEAD/mllp/models.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"1278025082c02427"}},{"code_sha256_prefix":"94022e8cc3f3e5d5","entry":"DisjunctionLayer","repo":"12wang3/mllp","repo_kind":"listed","path":"mllp/models.py","file_url":"https://github.com/12wang3/mllp/blob/HEAD/mllp/models.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"94022e8cc3f3e5d5"}},{"code_sha256_prefix":"e862e3c63e793da2","entry":"MLLP","repo":"12wang3/mllp","repo_kind":"listed","path":"mllp/models.py","file_url":"https://github.com/12wang3/mllp/blob/HEAD/mllp/models.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"e862e3c63e793da2"}},{"code_sha256_prefix":"3034df92a07331f3","entry":"RandomBinarizationLayer","repo":"12wang3/mllp","repo_kind":"listed","path":"mllp/models.py","file_url":"https://github.com/12wang3/mllp/blob/HEAD/mllp/models.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"3034df92a07331f3"}},{"code_sha256_prefix":"ef48289d401e500f","entry":"RandomlyBinarize","repo":"12wang3/mllp","repo_kind":"listed","path":"mllp/models.py","file_url":"https://github.com/12wang3/mllp/blob/HEAD/mllp/models.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"ef48289d401e500f"}},{"code_sha256_prefix":"44053310cb448478","entry":"UnionFind","repo":"12wang3/mllp","repo_kind":"listed","path":"mllp/models.py","file_url":"https://github.com/12wang3/mllp/blob/HEAD/mllp/models.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"44053310cb448478"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}