Human-in-the-loop construction of decision tree classifiers with parallel coordinates

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• dc.contributor.author Estivill-Castro, V. (Vladimir)
• dc.contributor.author Gilmore, Eugene
• dc.contributor.author Hexel, René
• dc.date.accessioned 2021-07-06T08:16:17Z
• dc.date.available 2021-07-06T08:16:17Z
• dc.date.issued 2020
• dc.description Comunicació presentada a: 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), celebrat del 11 al 14 d'octubre de 2020 a Toronto, Canadà.
• dc.description.abstract How can there be Human-In-the-Loop-Learning (HILL) if datasets aimed at building classifiers have ever more dimensions? We make two contributions. First, we examine the few early results on the effectiveness of HILL for building autonomous classifiers and report on our own experiment that validates the merits of HILL. Second, we introduce a HILL system (by using parallel coordinates) for learning of decision tree classifiers (DTCs). DTCs importantly emphasise the relevance of attributes and enable attribute selection, and therefore are appreciated for their transparency. The proposed system addresses a number of the shortcomings of the many HILL systems and allows for easy exploration of datasets. In particular, we incorporate parallel coordinates effectively in our tool for visualisation of high dimensional datasets. We can not only focus the learning on the accuracy of classifiers, but we can enhance performance in other important factors such as system's interpretability and the ability to gain insight into datasets. Finally, we show the advantages of our HILL system in the application area of mobile robotics using the case study of image segmentation in robotic soccer.en
• dc.format.mimetype application/pdf
• dc.identifier.citation Estivill-Castro V, Gilmore E, Hexel R. Human-in-the-loop construction of decision tree classifiers with parallel coordinates. In: 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC); 2020 Oct 11-14; Toronto, Canada. New Jersey: IEEE; 2020. p. 3852-9. DOI: 10.1109/SMC42975.2020.9283240
• dc.identifier.doi http://dx.doi.org/10.1109/SMC42975.2020.9283240
• dc.identifier.issn 2577-1655
• dc.identifier.uri http://hdl.handle.net/10230/48091
• dc.language.iso eng
• dc.publisher Institute of Electrical and Electronics Engineers (IEEE)
• dc.relation.ispartof 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC); 2020 Oct 11-14; Toronto, Canada. New Jersey: IEEE; 2020. p. 3852-9
• dc.rights © 2020 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 collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. http://dx.doi.org/10.1109/SMC42975.2020.9283240
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Visualizationen
• dc.subject.keyword Robot kinematicsen
• dc.subject.keyword Buildingsen
• dc.subject.keyword Machine learningen
• dc.subject.keyword Toolsen
• dc.subject.keyword Decision treesen
• dc.subject.keyword Task analysisen
• dc.title Human-in-the-loop construction of decision tree classifiers with parallel coordinatesen
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/acceptedVersion