[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124909-en":3,"doc-seo-124909-105":30,"detail-sidebar-cat-0-en-105":91},{"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":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124909,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Conformal Predictions for Probabilistically Robust Scalable Machine Learning Classification - Research paper","Conformal predictions establish reliable, robust learning algorithms by providing confidence guarantees that indicate whether a model is sufficiently accurate for real-world deployment. The work links classification with scalable classifiers, integrating statistical order theory and probabilistic learning to ground conformal guarantees from the outset. A new score function definition and a conformal safety set are introduced to locate input-space regions where error coverage is guaranteed. The paper demonstrates practical relevance in cybersecurity by identifying DNS tunneling attacks and supports the development of probabilistically robust AI models.","arXiv :2403 . 10368v1 [ stat .ML] 15 Mar 2024  \nConformal Predictions for Probabilistically Robust Scalable Machine Learning Classification  \nAlberto Carlevaro 1,4*, Teodoro Alamo3 , Fabrizio Dabbene2 ,  \nMaurizio Mongelli 1  \n1* Istituto di Elettronica e di Ingegneria dell’Informazione e delle Telecomunicazioni, CNR, Corso Ferdinando Maria Perrone, 24, Genoa,  \n16152, Italy.  \n2 Istituto di Elettronica e di Ingegneria dell’Informazione e delle Telecomunicazioni, CNR, Corso Duca degli Abruzzi, 24, Turin, 10129,  \nItaly.  \n3 Departamento de Ingenier`ıa de Sistemas y Autom`atica, Universidad de Sevilla, Escuela Superior de Ingenieros, Camino de los Descubrimientos,  \nSeville, 41092, Spain.  \n4 Funded Research Department, Aitek SpA, Via della Crocetta 15, Genoa, 16122, Italy.  \n*Corresponding author. E-mail(s): alberto.carlevaro@ieiit.cnr.it; Contributing authors: [talamo@us.es](talamo@us.es) ; [fabrizio.dabbene@cnr.it](fabrizio.dabbene@cnr.it) ;  \nmaurizio.mongelli@ieiit.cnr.it;  \nAbstract  \nConformal predictions make it possible to define reliable and robust learning algorithms. But they are essentially a method for evaluating whether an algorithm is good enough to be used in practice. To define a reliable learning framework for classification from the very beginning of its design, the concept of scalable classifier was introduced to generalize the concept of classical classifier by linking it to statistical order theory and probabilistic learning theory. In this paper, we analyze the similarities between scalable classifiers and conformal predictions by introducing a new definition of a score function and defining a special set of input variables, the conformal safety set, which can identify patterns in the input space that satisfy the error coverage guarantee, i.e., that the probability of observing the wrong (possibly unsafe) label for points belonging to this set is bounded by a predefined ε error level. We demonstrate the practical implications of this framework through an application in cybersecurity for identifying DNS tunneling  \n1  \nattacks. Our work contributes to the development of probabilistically robust and reliable machine learning models.  \nKeywords: Conformal predictions, Scalable classifiers, Confidence bounds, Robust AI  \n1 Introduction  \n1.1 Context  \nConformal predictions (CPs) [1] are gaining increasing importance in machine learning (ML) since they validate algorithms in terms of confidence of the prediction. Although it is a fairly recent field of study, there has been an astonishing production of scholarly papers, from the definition of new score functions to different methodologies for constructing conformal sets and, of course, a wide variety of applications. In fact, the ferment of scientific research in this field is so active that even the father of this theory, V. Vovk 1 , continues to actively contribute to the improvement of its knowledge, as in the case of [3] where he and his colleagues investigate the concept of validity under nonparametric hypotheses or the innovative introduction of Venn predictors as in [4] . We refer the reader to the surveys [5–7] that largely cover all recent publications and discussions on uncertainty quantification (UQ) through CP for machine learning models.  \nUnder canonical CP theory, the definition of a score function is very peculiar to either the classifier or the application at hand. For example,[8] defines a special conformity measure (corresponding to a score function), based on the residual between the calibration points and the classification hyperplane of a SVM model. Other example, always SVM-based, can be found in [8], [1] and [9], where different definitions of score function (or conformity/non-conformity measure) are given. One of the strengths of our approach, as will become clear later, is the unique definition of such a score function, which, given any classifier, allows the conformal prediction framework to be applied in the most natural way. The w","cbCaic4tgJLYm0w6","https://ap.wps.com/l/cbCaic4tgJLYm0w6","pdf",2297292,1,19,"English","en",105,"# Abstract\n# Introduction\n## Context\n## Contribution\n# Methodology and Theoretical Framework\n## Scalable classifiers and score functions\n## Conformal safety region and safety set\n## Error control via confidence level and epsilon\n# Applications\n## Cybersecurity: DNS tunneling attack identification","[{\"question\":\"What problem does conformal predictions solve in machine learning classification?\",\"answer\":\"It provides confidence guarantees to assess whether a classification algorithm is reliable enough for practical use by controlling prediction error coverage.\"},{\"question\":\"How does the paper connect scalable classifiers with conformal predictions?\",\"answer\":\"It exploits scalable classifiers fθ(x,ρ) to construct new score functions derived from the classifier geometry, enabling an explicit relationship between input space and conformity guarantees.\"},{\"question\":\"What is the conformal safety set and what guarantee does it provide?\",\"answer\":\"The conformal safety set Sε identifies regions of the input space where the probability of observing an incorrect (possibly unsafe) label is bounded by a predefined error level ε.\"}]","Conformal Predictions for Probabilistically Robust Scalable Machine Learning Classification - 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