Evaluation of Feature Selection Methods on Psychosocial Education Data Using Additive Ratio Assessment

Artificial intelligence, particularly machine learning, is the fastest-growing research trend in educational fields. Machine learning shows an impressive performance in many prediction models, including psychosocial education. The capability of machine learning to discover hidden patterns in large d...

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Bibliographic Details
Published in:Electronics (Basel) 2022-01, Vol.11 (1), p.114
Main Authors: Muttakin, Fitriani, Wang, Jui-Tang, Mulyanto, Mulyanto, Leu, Jenq-Shiou
Format: Article
Language:English
Subjects:
Academic grading
Accuracy
Artificial intelligence
Big Data
Classification
Datasets
Decision making
Education
Feature selection
Internet of Things
Machine learning
Methods
Multiple criterion
Performance measurement
Prediction models
Risk levels
Sparsity
Stress
Support vector machines
Variance analysis
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Summary:Artificial intelligence, particularly machine learning, is the fastest-growing research trend in educational fields. Machine learning shows an impressive performance in many prediction models, including psychosocial education. The capability of machine learning to discover hidden patterns in large datasets encourages researchers to invent data with high-dimensional features. In contrast, not all features are needed by machine learning, and in many cases, high-dimensional features decrease the performance of machine learning. The feature selection method is one of the appropriate approaches to reducing the features to ensure machine learning works efficiently. Various selection methods have been proposed, but research to determine the essential subset feature in psychosocial education has not been established thus far. This research investigated and proposed methods to determine the best feature selection method in the domain of psychosocial education. We used a multi-criteria decision system (MCDM) approach with Additive Ratio Assessment (ARAS) to rank seven feature selection methods. The proposed model evaluated the best feature selection method using nine criteria from the performance metrics provided by machine learning. The experimental results showed that the ARAS is promising for evaluating and recommending the best feature selection method for psychosocial education data using the teacher’s psychosocial risk levels dataset.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics11010114