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Feature Reduction Based on Hybrid Efficient Weighted Gene Genetic Algorithms with Artificial Neural Network for Machine Learning Problems in the Big Data
المؤلفون المشاركون
Bayat, Oguz
Ucan, Osman N.
Mohammed, Tareq Abed
Alhayali, Shaymaa
المصدر
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-10، 10ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-10-30
دولة النشر
مصر
عدد الصفحات
10
التخصصات الرئيسية
الملخص EN
A large amount of data being generated from different sources and the analyzing and extracting of useful information from these data becomes a very complex task.
The difficulty of dealing with big data arises from many factors such as the high number of features, existence of lost data, and variety of data.
One of the most effective solutions that used to overcome the huge amount of big data is the feature reduction process.
In this paper, a set of hybrid and efficient algorithms are proposed to classify the datasets that have large feature size by merging the genetic algorithms with the artificial neural networks.
The genetic algorithms are used as a prestep to significantly reduce the feature size of the analyzed data before handling that data using machine learning techniques.
Reducing the number of features simplifies the task of classifying the analyzed data and enhances the performance of the machine learning algorithms that are used to extract valuable information from big data.
The proposed algorithms use a new gene-weight mechanism that can significantly enhance the performance and decrease the required search time.
The proposed algorithms are applied on different datasets to pick the most relative and important features before applying the artificial neural networks algorithm, and the results show that our proposed algorithms can effectively enhance the classifying performance over the tested datasets.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Mohammed, Tareq Abed& Alhayali, Shaymaa& Bayat, Oguz& Ucan, Osman N.. 2018. Feature Reduction Based on Hybrid Efficient Weighted Gene Genetic Algorithms with Artificial Neural Network for Machine Learning Problems in the Big Data. Scientific Programming،Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1214656
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Mohammed, Tareq Abed…[et al.]. Feature Reduction Based on Hybrid Efficient Weighted Gene Genetic Algorithms with Artificial Neural Network for Machine Learning Problems in the Big Data. Scientific Programming No. 2018 (2018), pp.1-10.
https://search.emarefa.net/detail/BIM-1214656
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Mohammed, Tareq Abed& Alhayali, Shaymaa& Bayat, Oguz& Ucan, Osman N.. Feature Reduction Based on Hybrid Efficient Weighted Gene Genetic Algorithms with Artificial Neural Network for Machine Learning Problems in the Big Data. Scientific Programming. 2018. Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1214656
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1214656
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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