[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123027-en":3,"doc-seo-123027-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},123027,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",6,"Technology","Optimizing Binary Decision Diagrams for Interpretable Machine Learning Classification","Machine learning increasingly supports decision-making, creating strong demand for explainable models that humans can understand. This paper introduces Binary Decision Diagrams as interpretable binary-classification models, positioning them as decision-tree-like structures with often more compact representations enabled by node sharing. Fixed variable ordering supports concise explanations. A SAT-based learning method is proposed to obtain minimum-size BDDs with perfect training accuracy, alongside heuristic strategies to improve scalability.","POLITECNICO DI TORINO  \nRepository ISTITUZIONALE  \nOptimizing Binary Decision Diagrams for Interpretable Machine Learning Classification  \nOriginal  \nOptimizing Binary Decision Diagrams for Interpretable Machine Learning Classification / Cabodi, Gianpiero; Camurati, Paolo E. ; Marques-Silva, Joao; Palena, Marco; Pasini, Paolo. -In: IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS. -ISSN 0278-0070. -ELETTRONICO. -43:10(2024), pp. 3083- 3087. [10 . 1109/tcad.2024.3387876]  \nAvailability:  \nThis version is available at: 11583/2987831 since: 2024-04-15T11:24:40Z  \nPublisher: IEEE  \nPublished  \nDOI:10.1109/tcad.2024.3387876  \nTerms of use:  \nThis article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository  \nPublisher copyright  \nIEEE postprint/Author's Accepted Manuscript  \n©2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collecting works, for resale or lists, or reuse of any copyrighted component of this work in other works.  \n(Article begins on next page)  \n28 March 2025  \nThis article has been accepted for publication in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. This is the author's version which has not been fully edi content may change prior to final publication. Citation information: DOI 10. 1109/TCAD.2024.3387876  \nOptimizing Binary Decision Diagrams for Interpretable Machine Learning Classification  \nGianpiero Cabodi∗ , Paolo E. Camurati∗ , Joao Marques-Silva†, Marco Palena∗ and Paolo Pasini∗  \n∗ DAUIN, Politecnico di Torino, Turin, IT  \n{gianpiero.cabodi,paolo.camurati,marco.palena,paolo.pasini}@polito.it  \n†ANITI, University of Toulouse, Toulouse, FR  \n[joao.marques-silva@univ-toulouse.fr](joao.marques-silva@univ-toulouse.fr)  \nAbstract—Machine learning (ML) is ever more frequently used as a tool to aid decision-making. The need to understand the decisions made by ML algorithms has sparked a renewed interest in explainable ML models. A number of known models are often regarded as interpretable by human decision-makers with varying degrees of difficulty. The size of such models plays a crucial role in determining how easily they can be understood by a human. In this paper1 we propose the use of Binary Decision Diagrams (BDDs) as an interpretable ML model. BDDs can be deemed as interpretable as decision trees (DTs) while offering a often more compact representation due to node sharing. Fixed variable ordering also allows for more concise explanations. We propose a SAT-based approach for learning optimal BDDs that exhibit perfect accuracy on training data. We also explore heuristic methods for computing sub-optimal BDDs, in order to improve scalability.  \nI. INTRODUCTION  \nThe increasing use of Machine Learning (ML) to automate decision-making motivates the need to explain the outcome of ML algorithms to the general public. The importance of this“right to explanation” has been recognized in recent legislative efforts [2] . Social acceptance of ML-made decisions entails going beyond the classical black-box model. The area of eXplainable Artificial Intelligence (XAI) [3] responds to such a need by providing models whose outcomes can be easily explained to human decision-makers.  \nIn this paper we focus on interpretable models for binary classification problems [4] . In such a context, ML models can be seen as formalisms to represent Boolean functions that map binary features to one of two classes. Several known classification models can associate predictions with explanations, such as decision lists (DLs), decision sets (DSs) and decision trees (DTs) [5], among others. The size of a ML model plays a critical role in interpretability, with smaller representations leading to more concise and easier ","cbCaiuPqTH4gLXBf","https://ap.wps.com/l/cbCaiuPqTH4gLXBf","pdf",262212,1,"English","en",105,"# Introduction\n## Related Works\n## Contribution","[{\"question\":\"Why are explainable machine learning models needed in binary classification?\",\"answer\":\"The growing use of ML for decision automation creates a need to explain outcomes to the public and support a “right to explanation.” Interpretable binary-classification models help human decision-makers understand predictions.\"},{\"question\":\"What makes Binary Decision Diagrams (BDDs) interpretable compared with decision trees?\",\"answer\":\"BDDs are comparable to decision trees through their path-based explanations, while fixed variable ordering and node sharing often yield more compact representations. This can produce shorter and more concise explanations.\"},{\"question\":\"How does the paper learn optimal and scalable BDD-based classifiers?\",\"answer\":\"It proposes a SAT-based approach to learn minimum-size BDDs with perfect accuracy on training data, which works well for smaller problems. For larger settings, it also explores heuristic methods to compute sub-optimal BDDs and improve scalability.\"}]","Optimizing Binary Decision Diagrams for Interpretable Machine Learning Classification | PDF",1785814244,15,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"optimizing-binary-decision-diagrams-for-interpretable-machine-learning-classification","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/technology/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/optimizing-binary-decision-diagrams-for-interpretable-machine-learning-classification/123027/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Why are explainable machine learning models needed in binary classification?","Question",{"text":74,"@type":75},"The growing use of ML for decision automation creates a need to explain outcomes to the public and support a “right to explanation.” Interpretable binary-classification models help human decision-makers understand predictions.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What makes Binary Decision Diagrams (BDDs) interpretable compared with decision trees?",{"text":79,"@type":75},"BDDs are comparable to decision trees through their path-based explanations, while fixed variable ordering and node sharing often yield more compact representations. This can produce shorter and more concise explanations.",{"name":81,"@type":72,"acceptedAnswer":82},"How does the paper learn optimal and scalable BDD-based classifiers?",{"text":83,"@type":75},"It proposes a SAT-based approach to learn minimum-size BDDs with perfect accuracy on training data, which works well for smaller problems. 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