Abstract
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 language | English |
|---|---|
| Article number | 421642 |
| Journal | Mathematical Problems in Engineering |
| Volume | 2015 |
| DOIs | |
| Publication status | Published - 2015 |
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ASJC Scopus subject areas
- Mathematics(all)
- Engineering(all)
Cite this
The Effects of Feature Optimization on High-Dimensional Essay Data. / Yi, Bong Jun; Lee, Do Gil; Rim, Hae-Chang.
In: Mathematical Problems in Engineering, Vol. 2015, 421642, 2015.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - The Effects of Feature Optimization on High-Dimensional Essay Data
AU - Yi, Bong Jun
AU - Lee, Do Gil
AU - Rim, Hae-Chang
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=84945906056&partnerID=8YFLogxK
U2 - 10.1155/2015/421642
DO - 10.1155/2015/421642
M3 - Article
AN - SCOPUS:84945906056
VL - 2015
JO - Mathematical Problems in Engineering
JF - Mathematical Problems in Engineering
SN - 1024-123X
M1 - 421642
ER -