کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6962585 1452271 2016 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Improving scenario discovery for handling heterogeneous uncertainties and multinomial classified outcomes
ترجمه فارسی عنوان
بهبود کشف سناریو برای رفع ناهماهنگی های ناهمگونی و نتایج طبقه بندی چندجملهای
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزار
چکیده انگلیسی
Scenario discovery is a novel model-based approach to scenario development in the presence of deep uncertainty. Scenario discovery frequently relies on the Patient Rule Induction Method (PRIM). PRIM identifies regions in the model input space that are highly predictive of producing model outcomes that are of interest. To identify these, PRIM uses a lenient hill climbing optimization procedure. PRIM struggles when confronted with cases where the uncertain factors are a mix of data types, and can be used only for binary classifications. We compare two more lenient objective functions which both address the first problem, and an alternative objective function using Gini impurity which addresses the second problem. We assess the efficacy of the modification using previously published cases. Both modifications are effective. The more lenient objective functions produce better descriptions of the data, while the Gini impurity objective function allows PRIM to be used when handling multinomial classified data.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Environmental Modelling & Software - Volume 79, May 2016, Pages 311-321
نویسندگان
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