کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
412820 679683 2017 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Approximate k-NN delta test minimization method using genetic algorithms: Application to time series
ترجمه فارسی عنوان
روش تقریبی K-NN حداقل سازی آزمون دلتا با استفاده از الگوریتم ژنتیک: برنامه ای برای سری های زمانی
کلمات کلیدی
الگوریتم ژنتیک؛ آزمون دلتا؛ انتخاب متغیر؛ تقریبی K- نزدیکترین همسایه ها؛ پوسته پوسته شدن متغیر؛ طرح ریزی متغیر؛ سری زمانی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی

In many real world problems, the existence of irrelevant input variables (features) hinders the predictive quality of the models used to estimate the output variables. In particular, time series prediction often involves building large regressors of artificial variables that can contain irrelevant or misleading information. Many techniques have arisen to confront the problem of accurate variable selection, including both local and global search strategies. This paper presents a method based on genetic algorithms that intends to find a global optimum set of input variables that minimize the Delta Test criterion. The execution speed has been enhanced by substituting the exact nearest neighbor computation by its approximate version. The problems of scaling and projection of variables have been addressed. The developed method works in conjunction with MATLAB's Genetic Algorithm and Direct Search Toolbox. The goodness of the proposed methodology has been evaluated on several popular time series examples, and also generalized to other non-time-series datasets.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 73, Issues 10–12, June 2010, Pages 2017–2029
نویسندگان
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