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http://dx.doi.org/10.6109/jkiice.2021.25.1.56

Comparative Analysis of the Binary Classification Model for Improving PM10 Prediction Performance  

Jung, Yong-Jin (Department of Electrical, Electronics and Communication Engineering, Korea University of Technology and Education(KOREATECH))
Lee, Jong-Sung (Department of Electrical, Electronics and Communication Engineering, Korea University of Technology and Education(KOREATECH))
Oh, Chang-Heon (Department of Electrical, Electronics and Communication Engineering, Korea University of Technology and Education(KOREATECH))
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.1, 2021 , pp. 56-62 More about this Journal
Abstract
High forecast accuracy is required as social issues on particulate matter increase. Therefore, many attempts are being made using machine learning to increase the accuracy of particulate matter prediction. However, due to problems with the distribution of imbalance in the concentration and various characteristics of particulate matter, the learning of prediction models is not well done. In this paper, to solve these problems, a binary classification model was proposed to predict the concentration of particulate matter needed for prediction by dividing it into two classes based on the value of 80㎍/㎥. Four classification algorithms were utilized for the binary classification of PM10. Classification algorithms used logistic regression, decision tree, SVM, and MLP. As a result of performance evaluation through confusion matrix, the MLP model showed the highest binary classification performance with 89.98% accuracy among the four models.
Keywords
Machine learning; Artificial neural network; Particulate matter; Classification;
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