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The Effects of Feature Optimization on High-Dimensional Essay Data
Joint Authors
Yi, Bong-Jun
Lee, Do-Gil
Rim, Hae-Chang
Source
Mathematical Problems in Engineering
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-10-12
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Yi, Bong-Jun& Lee, Do-Gil& Rim, Hae-Chang. 2015. The Effects of Feature Optimization on High-Dimensional Essay Data. Mathematical Problems in Engineering،Vol. 2015, no. 2015, pp.1-12.
https://search.emarefa.net/detail/BIM-1073798
Modern Language Association (MLA)
Yi, Bong-Jun…[et al.]. The Effects of Feature Optimization on High-Dimensional Essay Data. Mathematical Problems in Engineering No. 2015 (2015), pp.1-12.
https://search.emarefa.net/detail/BIM-1073798
American Medical Association (AMA)
Yi, Bong-Jun& Lee, Do-Gil& Rim, Hae-Chang. The Effects of Feature Optimization on High-Dimensional Essay Data. Mathematical Problems in Engineering. 2015. Vol. 2015, no. 2015, pp.1-12.
https://search.emarefa.net/detail/BIM-1073798
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1073798