کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6883468 1444173 2018 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Novel classifiers for intelligent disease diagnosis with multi-objective parameter evolution
ترجمه فارسی عنوان
طبقه بندی های رمان برای تشخیص بیماری های هوشمند با تکامل چند هدفه
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر شبکه های کامپیوتری و ارتباطات
چکیده انگلیسی
In this research, intelligent classifiers for disease diagnosis are designed that use classifier parameters, such as cost, tolerance, gamma and epsilon, with multi-objective evolutionary algorithms. The multiple objective functions are prediction accuracy, sensitivity and specificity. This paper employs a Sequential Minimal Optimization (SMO), a variant of the classical Support Vector Machine (SVM), as the base classifier in conjunction with three popular evolutionary algorithms (EA), namely, Elephant Herding Optimization (EHO), Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II), for parameter evolution. A new cuboids based initial population generation mechanism was also introduced to hybridize EHO, called CEHO. The performance of CEHO is compared with the other three EAs (EHO, MOEA/D and NSGA-II) over 17 medical engineering datasets, and pertinent statistical tests were conducted to substantiate their performances. The results demonstrate that the proposed CEHO exhibit better to competitive results across all datasets.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers & Electrical Engineering - Volume 67, April 2018, Pages 483-496
نویسندگان
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