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A framework for profiling spatial variability in the performance of classification models

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dc.contributor.authorWarushavithana, Menuka, author
dc.contributor.authorBarram, Kassidy, author
dc.contributor.authorCarlson, Caleb, author
dc.contributor.authorMitra, Saptashwa, author
dc.contributor.authorGhosh, Sudipto, author
dc.contributor.authorBreidt, Jay, author
dc.contributor.authorPallickara, Sangmi Lee, author
dc.contributor.authorPallickara, Shrideep, author
dc.contributor.authorACM, publisher
dc.date.accessioned2024-11-11T19:34:35Z
dc.date.available2024-11-11T19:34:35Z
dc.date.issued2024-04-03
dc.description.abstractScientists use models to further their understanding of phenomena and inform decision-making. A confluence of factors has contributed to an exponential increase in spatial data volumes. In this study, we describe our methodology to identify spatial variation in the performance of classification models. Our methodology allows tracking a host of performance measures across different thresholds for the larger, encapsulating spatial area under consideration. Our methodology ensures frugal utilization of resources via a novel validation budgeting scheme that preferentially allocates observations for validations. We complement these efforts with a browser-based, GPU-accelerated visualization scheme that also incorporates support for streaming to assimilate validation results as they become available.
dc.format.mediumborn digital
dc.format.mediumarticles
dc.identifier.bibliographicCitationMenuka Warushavithana, Kassidy Barram, Caleb Carlson, Saptashwa Mitra, Sudipto Ghosh, Jay Breidt, Sangmi Lee Pallickara, and Shrideep Pallickara. 2023. A Framework for Profiling Spatial Variability in the Performance of Classification Models. In IEEE/ACM 10th International Conference on Big Data Computing, Applications and Technologies (BDCAT '23), December 4–7, 2023, Taormina (Messina), Italy. ACM, Taormina, Italy, 11 pages. https://doi.org/10.1145/3632366.3632387
dc.identifier.doihttps://doi.org/10.1145/3632366.3632387
dc.identifier.urihttps://hdl.handle.net/10217/239542
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartofPublications
dc.relation.ispartofACM DL Digital Library
dc.rights©Menuka Warushavithana, et al. ACM 2023. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in BDCAT '23, https://dx.doi.org/10.1145/3632366.3632387.
dc.subjectspatial data
dc.subjectmodel validations
dc.subjectclassification
dc.subjectvisual analytics
dc.titleA framework for profiling spatial variability in the performance of classification models
dc.typeText

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