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Browsing by Author "Warushavithana, Menuka, author"

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    A framework for profiling spatial variability in the performance of classification models
    (Colorado State University. Libraries, 2024-04-03) Warushavithana, Menuka, author; Barram, Kassidy, author; Carlson, Caleb, author; Mitra, Saptashwa, author; Ghosh, Sudipto, author; Breidt, Jay, author; Pallickara, Sangmi Lee, author; Pallickara, Shrideep, author; ACM, publisher
    Scientists 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.
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    Containerization of model fitting workloads over spatial datasets
    (Colorado State University. Libraries, 2021) Warushavithana, Menuka, author; Pallickara, Shrideep, advisor; Pallickara, Sangmi, advisor; Breidt, Jay, committee member
    Spatial data volumes have grown exponentially over the past several years. The number of domains in which spatial data are extensively leveraged include atmospheric sciences, environmental monitoring, ecological modeling, epidemiology, sociology, commerce, and social media among others. These data are often used to understand phenomena and inform decision making by fitting models to them. In this study, we present our methodology to fit models at scale over spatial data. Our methodology encompasses segmentation, spatial similarity based on the dataset(s) under consideration, and transfer learning schemes that are informed by the spatial similarity to train models faster while utilizing fewer resources. We consider several model fitting algorithms and execution within containerized environments as we profile the suitability of our methodology. Our benchmarks validate the suitability of our methodology to facilitate faster, resource-efficient training of models over spatial data.