دانلود مقاله ISI انگلیسی شماره 79656
عنوان فارسی مقاله

نسل ویژگی با استفاده از برنامه نویسی ژنتیک با انتخاب شریک زندگی تطبیقی برای طبقه بندی دیابت

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
79656 2013 11 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Feature generation using genetic programming with comparative partner selection for diabetes classification
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Expert Systems with Applications, Volume 40, Issue 13, 1 October 2013, Pages 5402–5412

کلمات کلیدی
دیابت هند Pima؛ برنامه نویسی ژنتیک؛ انتخاب شریک زندگی مقایسه
پیش نمایش مقاله
پیش نمایش مقاله نسل ویژگی با استفاده از برنامه نویسی ژنتیک با انتخاب شریک زندگی تطبیقی برای طبقه بندی دیابت

چکیده انگلیسی

The ultimate aim of this research is to facilitate the diagnosis of diabetes, a rapidly increasing disease in the world. In this research a genetic programming (GP) based method has been used for diabetes classification. GP has been used to generate new features by making combinations of the existing diabetes features, without prior knowledge of the probability distribution. The proposed method has three stages: features selection is performed at the first stage using t-test, Kolmogorov–Smirnov test, Kullback–Leibler divergence test, F-score selection, and GP. The results of feature selection methods are used to prepare an ordered list of original features where features are arranged in decreasing order of importance. Different subsets of original features are prepared by adding features one by one in each subset using sequential forward selection method according to the ordered list. At the second stage, GP is used to generate new features from each subset of original diabetes features, by making non-linear combinations of the original features. A variation of GP called GP with comparative partner selection (GP-CPS), utilising the strengths and the weaknesses of GP generated features, has been used at the second stage. The performance of GP generated features for classification is tested using the k-nearest neighbor and support vector machine classifiers at the last stage. The results and their comparisons with other methods demonstrate that the proposed method exhibits superior performance over other recent methods.

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پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.