کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6854952 1437600 2018 38 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Dynamic selection of normalization techniques using data complexity measures
ترجمه فارسی عنوان
انتخاب پویا از تکنیک های عادی با استفاده از اندازه گیری پیچیدگی داده ها
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی
Data preprocessing is an important step for designing classification model. Normalization is one of the preprocessing techniques used to handle the out-of-bounds attributes. This work develops 14 classification models using different learning algorithms for dynamic selection of normalization technique. This work extracts 12 data complexity measures for 48 datasets drawn from the KEEL dataset repository. Each of these datasets is normalized using min-max and z-score normalization technique. G-mean index is estimated for these normalized datasets using Gaussian Kernel Extreme Learning Machine (KELM) in order to determine the best-suited normalization technique. The data complexity measures along with the best-suited normalization technique are used as an input for developing the aforementioned dynamic models. These models predict the best suitable normalization technique based on the estimated data complexity measures of the dataset. The result shows that the model developed using Gaussian Kernel ELM (KELM) and Support Vector Machine (SVM) give promising results for most of the evaluated classification problems.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 106, 15 September 2018, Pages 252-262
نویسندگان
, , ,