The Effects of Feature Optimization on High-Dimensional Essay Data

Bong Jun Yi, Do Gil Lee, Hae Chang Rim

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


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.

Original languageEnglish
Article number421642
JournalMathematical Problems in Engineering
Publication statusPublished - 2015

Bibliographical note

Publisher Copyright:
© 2015 Bong-Jun Yi et al.

ASJC Scopus subject areas

  • General Mathematics
  • General Engineering


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