کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8941774 | 1645027 | 2018 | 38 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
CHI-BD: A fuzzy rule-based classification system for Big Data classification problems
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
The previous Fuzzy Rule-Based Classification Systems (FRBCSs) for Big Data problems consist in concurrently learning multiple Chi et al. FRBCSs whose rule bases are then aggregated. The problem of this approach is that different models are obtained when varying the configuration of the cluster, becoming less accurate as more computing nodes are added. Our aim with this work is to design a new FRBCS for Big Data classification problems (CHI-BD) which is able to provide exactly the same model as the one that would be obtained by the original Chi et al. algorithm if it could be executed with this quantity of data. In order to do so, we take advantage of the suitability of the Chi et al. algorithm for the MapReduce paradigm, solving the problems of the previous approach, which lead us to obtain the same model (i.e., classification accuracy) regardless of the number of computing nodes considered.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Fuzzy Sets and Systems - Volume 348, 1 October 2018, Pages 75-101
Journal: Fuzzy Sets and Systems - Volume 348, 1 October 2018, Pages 75-101
نویسندگان
Mikel Elkano, Mikel Galar, Jose Sanz, Humberto Bustince,