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The Effects of Feature Optimization on High-Dimensional Essay Data

机译：特征优化对高维论文数据的影响

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摘要

Current machine learning (ML) based automated essay scoring (AES) systems have employed various and vast numbers of features, which have been proven to be useful, in improving the performance of the AES. However, the high-dimensional feature space is not properly represented, due to the large volume of features extracted from the limited training data. As a result, this problem gives rise to poor performance and increased training time for the system. In this paper, we experiment and analyze the effects of feature optimization, including normalization, discretization, and feature selection techniques for different ML algorithms, while taking into consideration the size of the feature space and the performance of the AES. Accordingly, we show that the appropriate feature optimization techniques can reduce the dimensions of features, thus, contributing to the efficient training and performance improvement of AES.
机译：当前的基于机器学习（ML）的自动作文评分（AES）系统已采用了多种功能，这些功能已被证明对改善AES的性能很有用。但是，由于从有限的训练数据中提取了大量的特征，因此无法正确地表示高维特征空间。结果，该问题导致系统的较差性能和增加的训练时间。在本文中，我们在考虑到特征空间的大小和AES的性能的同时，针对不同的ML算法对特征优化的效果进行了实验和分析，包括归一化，离散化和特征选择技术。因此，我们表明适当的特征优化技术可以减小特征的尺寸，从而有助于AES的有效训练和性能改进。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第20期|421642.1-421642.12|共12页
  • 作者

    Yi Bong-Jun; Lee Do-Gil; Rim Hae-Chang;

  • 作者单位

    Korea Univ, Dept Comp & Radio Commun Engn, Seoul 136713, South Korea;

    Korea Univ, Res Inst Korean Studies, Seoul 136713, South Korea;

    Korea Univ, Dept Comp & Radio Commun Engn, Seoul 136713, South Korea;

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