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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

机译：基于混合高效加权遗传算法和人工神经网络的大数据机器学习特征约简

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摘要

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.
机译：从不同来源生成大量数据，以及从这些数据中分析和提取有用信息成为一项非常复杂的任务。处理大数据的困难源于许多因素，例如功能数量众多，丢失数据的存在以及数据的多样性。用来克服大量大数据的最有效解决方案之一是功能缩减过程。本文提出了一套混合高效算法，通过将遗传算法与人工神经网络融合，对具有较大特征量的数据集进行分类。遗传算法被用作在使用机器学习技术处理数据之前显着减小分析数据的特征尺寸的前奏。减少特征数量简化了对分析数据进行分类的任务，并增强了用于从大数据中提取有价值的信息的机器学习算法的性能。所提出的算法使用一种新的基因权重机制，可以显着提高性能并减少所需的搜索时间。所提出的算法在应用人工神经网络算法之前，已在不同的数据集上选取了最相关和最重要的特征，结果表明，所提出的算法可以有效地提高测试数据集的分类性能。

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  • 来源
    《Scientific programming》 |2018年第2期|2691759.1-2691759.10|共10页
  • 作者

    Mohammed Tareq Abed; Alhayali Shaymaa; Bayat Oguz; Ucan Osman N.;

  • 作者单位

    Altinbas Univ, Coll Engn, Istanbul, Turkey|Kirkuk Univ, Coll Sci, Kirkuk, Iraq;

    Altinbas Univ, Coll Engn, Istanbul, Turkey;

    Altinbas Univ, Coll Engn, Istanbul, Turkey;

    Altinbas Univ, Coll Engn, Istanbul, Turkey;

  • 收录信息 美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
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