Comparative analysis of multiple classification models to improve PM10 prediction performance

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 11, No. 3, June 2021, pp. 2500~2507
ISSN: 2088-8708, DOI: 10.11591/ijece.v11i3.pp2500-2507  2500
Journal homepage: http://ijece.iaescore.com
Comparative analysis of multiple classification models to
improve PM10 prediction performance
Yong-Jin Jung, Kyoung-Woo Cho, Jong-Sung Lee, Chang-Heon Oh
Department of Electrical, Electronics and Communication Engineering, Korea University of Technology and Education
(KOREATECH), Korea
Article Info ABSTRACT
Article history:
Received Jul 31, 2020
Revised Sep 22, 2020
Accepted Oct 14, 2020
With the increasing requirement of high accuracy for particulate matter
prediction, various attempts have been made to improve prediction accuracy
by applying machine learning algorithms. However, the characteristics of
particulate matter and the problem of the occurrence rate by concentration
make it difficult to train prediction models, resulting in poor prediction. In
order to solve this problem, in this paper, we proposed multiple classification
models for predicting particulate matter concentrations required for
prediction by dividing them into AQI-based classes. We designed multiple
classification models using logistic regression, decision tree, SVM and
ensemble among the various machine learning algorithms. The comparison
results of the performance of the four classification models through error
matrices confirmed the f-score of 0.82 or higher for all the models other than
the logistic regression model.
Keywords:
Classification
Decision tree
Ensemble
Logistic regression
Machine learning
Particulate matter
Support vector machine
This is an open access article under the CC BY-SA license.
Corresponding Author:
Chang-Heon Oh
Department of Electrical, Electronics and Communication Engineering
Korea University of Technology and Education (KOREATECH)
1600, Chungjeol-ro, Byeongcheon-myeon, Dongnam-gu, Cheonan-si, Chungcheongnam-do, 31253
Republic of Korea
Email: choh@koreatech.ac.kr
1. INTRODUCTION
Particulate matter is a substance made up of various sizes, shapes, and ingredients. Particulate
matter, divided into 𝑃𝑀10, 𝑃𝑀2.5 according to the size of 10𝜇𝑔, 2.5𝜇𝑔 or less, affects our health by causing
some diseases such as cardiovascular, respiratory, and cerebrovascular diseases. Accordingly, particulate
matter was classified as a dangerous substance, and it is analyzed as the cause of decreasing the vitality of
society members [1-7]. In order to avoid such harmful effects of particulate matter as much as possible, it has
become a routine practice to check the information provided based on the air quality index (AQI), which is
divided into four categories: 'good', 'moderate', 'bad', and 'very bad'. Korea's particulate matter prediction
accuracy was approximately 60% in 2015, and the Korea Meteorological Administration's prediction process
has the annual predicton accuracy of approximately 80%. However, this is information reflecting on the
weather forecaster's experience, and the actual particulate matter prediction model shows the accuracy of
approximately 50% [8, 9].
Therefore, various attempts have been made to improve the prediction accuracy of particulate matter
by applying machine learning prediction algorithms along with conventional statistical techniques [10, 11].
However, the characteristics of particulate matter arising from various external factors and the problem of the
the occurrence rate by concentration make it difficult to effectively train prediction models. In order to
improve the prediction accuracy of particulate matter concentrations, K. W. Cho et al., in their study,

Int J Elec & Comp Eng ISSN: 2088-8708 
Comparative analysis of multiple classification models to… (Yong-Jin Jung)
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proposed a prediction model that separated and predicted them based on a specific concentration. By dividing
the low and high concentrations based on the particulate matter concentration of 81𝜇𝑔, they compared the
prediction performance through a deep neural network-based prediction model. The prediction results
confirmed that the prediction performance of the low and high concentrations was improved, and especially it
showed the performance improvement of 20.62% for the high concentrations [9]. The study by K. Kaya et al.
proposed a solution to an unbalanced problem in order to address the prediction problem of the regression
model due to the variation in the occurrence rate by particulate matter concentration. They confirmed the
accuracy of approximately 80% in the entire data set by making the number of samples of the class the same
for unbalanced data through the proposed upper sampling and down sampling [12].
In this paper, we propose data classification models by concentration to improve the performance of
a particulate matter concentration prediction model. Of the machine learning classification models, we use
the logistic regression, decision tree, support vector machine (SVM), and ensemble models. Based on the
AQI, we configure multiple classification models by dividing particulate matter concentrations into 4 classes.
In order to apply the optimal parameters to the models, we design the models by performing parameter search
through grid search cross validation. We perform model evaluation using the error matrix.
2. DATA COLLECTION AND CONFIGURATION
2.1. Data collection and preprocessing
Particulate mattert is affected by various factors. Air pollutants and meteorological elements are
typical, which are commonly applied to studies for predicting particulate matter concentrations [13-16].
Based on the studies, we selected the major data as shown in Table 1.
Table 1. Major data definition
Type Name Description
Air
pollutants
𝑃𝑀10 The average particulate matter(< 10𝜇𝑚) per hour
𝑃𝑀10ℎ The average particulate matter(< 10𝜇𝑚) of the previous 1 hour
𝑂3 The average ozone of the previous 1 hour
𝐶𝑂 The average carbon monoxide of the previous 1 hour
𝑁𝑂2 The average nitrogen dioxide of the previous 1 hour
𝑆𝑂2 The average sulfur dioxide of the previous 1 hour
Meteorological
elements
Temperature The average temperature of the previous 1 hour
Humidity The average humidity of the previous 1 hour
Wind Speed The average wind speed of the previous 1 hour
Wind Direction The most frequent wind direction of the previous 1 hour
According to the selected data, we collected the final confirmed data measured at an interval of an
hour for 10 years from 2009 to 2018 at the measurement station around Cheonan in Korea. Air pollution data
is composed of 𝑃𝑀10, 𝑃𝑀10ℎ, 𝑂3, 𝐶𝑂, 𝑁O2, and 𝑆𝑂2, and meteorological elements consist of temperature,
humidity, wind speed, and wind direction. Since some data were missing due to the power outage and
maintenance of measurement equipment, we removed all data of the same time when the missing data was
present. Of the meteorological elements, the largest wind direction expressed in azimuth, that is, the 0° and
360°, which were often used with mixed notation, were unified to 360°.
There is a need for data preprocessing to perform classification through machine learning algorithms
using the collected data. Since we used the classification algorithm based on supervised learning, we
performed classification by separately dividing the data corresponding to independent variables and the data
corresponding to dependent variables. The independent variable data, which includes 𝑃𝑀10ℎ, 𝑂3, 𝐶𝑂, 𝑁O2,
𝑆𝑂2, temperature, humidity, wind speed and wind direction, is used to predict the range of particulate matter
concentrations based on the AQI. As for the wind direction, it is necessary to convert it to a vector form
because it corresponds to categorical data expressed in 16 directions. Therefore, through one-hot encoding,
we converted the categories corresponding to 16 directions to 16 vectors expressed in 0 and 1. For the
remaining input sample data other than the wind direction, they are numerical data with different
characteristics, and they were converted to a value between 0 and 1 through min max scaling in order to unify
the range of numerical values expressed according to the data. The dependent variable data correspond to
𝑃𝑀10, and as shown in Table 2, based on the AQI used as a forecast by the Ministry of Environment, we
divided 𝑃𝑀10 into the sequential categories: 'good', 'moderate', 'bad', and 'very bad' , and expressed them as
four classes of 0, 1, 2, and 3, respectively.

 ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 11, No. 3, June 2021 : 2500 - 2507
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Table 2. The range of particulate matter concentrations based on AQI
Grade Good Moderate Bad Very Bad
𝑃𝑀10(μg/𝑚3
) 0 ~ 30 31 ~ 80 81 ~ 150 150 ~
2.2. Data configuration
The data used in the supervised learning model is mainly composed of a training set for learning and
a test set for evaluating the trained model. The training set used to train the model is subdivided into a train
set and a validation set because of the need to verify whether training is well completed. In this paper, we
configure a training set of 75% and a test set of 25% with the preprocessed data. The training set is composed
of a train set of 80% and a validation set of 20%. Figure 1 shows the structure of the final data used in the
model, and Table 3 shows the configuration of the data set.
Figure 1. Structure of data set
Table 3. Data set configuration
Structure of data set Samples
Training set
Train set 52,315
Validation set 13,079
Test set 21,799
Total 87,193
3. CLASSIFICATION MODEL DESIGN
3.1. Logistic regression model design
Logistic regression is an algorithm used to predict the likelihood of an event using a linear
combination of independent variables. As in general regression analysis, it is used in future prediction models
by deriving a specific function through the relationship between dependent and independent variables.
However, unlike linear regression, since the prediction result is classified as a specific category when the
dependent variable is categorical data, it is used as a classification technique rather than a regression
technique. It is divided into binomial or multinomial depending on the category characteristics of the
dependent variable. The dependent variable for training the classification model of particulate matter
concentrations has four categories. Accordingly, we built a model by applying a multinomial logistic
regression method.
For a model predicting a certain result, overfitting or underfitting is contingent on the intensity of
training. It is difficult for a model with overfitting to predict new data since it only focuses on training data.
In the case of underfitting, there is a problem that the model does not predict most of the data since it does
not identify the characteristics of the data due to simple training. To solve these problems, logistic regression
basically uses L2 regularization parameter c [17].
Therefore, for better prediction performance, we performed the search for the optimal c value using
grid search cross validation to find the value of c that fits the model. We set the range of c values to be
searched to 0.0001, 0.001, 0.01, 0.1, 1, 10, 100, and 1000. In order to select parameters with high
generalization performance, we set the cv parameter of k-fold cross validation to 5. Accordingly, the c value
was sequentially accessed to compare scores using the test set after 5 repetitive training runs and validations.
For preprocessing of validation fold during cross validation, we searched the c values by building the
pipeline of min max scaler and the model. Table 4 shows the mean test score and c values of the top 3
rankings in the cross validation results. The cross validation results showed that the mean test score was
highest with 0.808958 when the c value was 10.0, thus we selected the c value to be applied to logistic
regression as 10.0.

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Table 4. Grid search cross validation results (logistic regression)
Rank Mean test score c
1 0.808958 10.0
2 0.808927 1000.0
3 0.806817 1.0
3.2. Decision tree model design
Decision tree is a widely used model for classification and regression. It is basically an algorithm
that learns by continuously answering questions to approach a specific decision. With the increase in the
number of leaf nodes, the accuracy of the training set increases but overfitting may occur [18]. One of the
methods used to prevent overfitting is to stop the growth of the tree when the depth of the tree reaches a
certain level. The parameter that limits the depth of a decision tree is max_depth, and we are able to improve
the performance of the model by adjusting the depth.
Therefore, we performed the search for the optimal max_depth value using grid search cross
validation. We set the range of max_depth values to be searched to 1~24, and performed the search by setting
the cv parameter of k-fold cross validation to 5. Additionally, for preprocessing of validation fold during
cross validation, we searched the max_depth values by building the pipeline of min max scaler and the
model. Table 5 shows the mean test score and max_depth values of the top 3 rankings in the cross validation
results. The cross validation results showed that the mean test score was highest with 0.85936013 when the
max_depth value was 4, thus we selected the max_depth value to be applied to decision tree as 4.
Table 5. Grid search cross validation results (decision tree)
Rank Mean test score Max_depth
1 0.85936013 4
2 0.85896255 5
3 0.85856496 3
3.3. SVM model design
SVM is one of machine learning methods and is a supervised learning model for pattern recognition
and data analysis. It is mainly used for classification and regression analysis. Given a set of data belonging to
one of two categories, it generates a non-stochastic binary linear classification model that determines the
category to which new data belongs based on a given data set. The generated classification model is
expressed as a boundary in the space onto which the data is mapped. It is an algorithm to find the boundary
with the largest width [19-21]. Therefore, SVM is a model that defines a baseline for classification between
categories, which is expressed as a decision boundary.
In SVM, the difference in performance is determined depending on how the decision boundary is
defined, and it is crucial to find the optimal decision boundary. The parameters applied to find the optimal
decision boundary are c and gamma. C is a parameter that adjusts the allowable range of outliers by
controlling the margin of the decision boundary, and gamma is a parameter that prevents overfitting of the
model by controlling the flexibility of the decision boundary.
We performed the search for the optimal c and gamma values to find the optimal decision boundary
using grid search cross validation. We set the range of c and gamma values to be searched to 0.0001, 0.001,
0.01, 0.1, 1, 10, 100, and 1000, and performed the search by setting the cv parameter of k-fold cross
validation to 5. Additionally, for preprocessing of validation fold during cross validation, we searched the c
and gamma values by building the pipeline of min max scaler and the model. Table 6 shows the mean test
score and relevant c and gamma values of the top 3 rankings in the cross validation results. The cross
validation results showed that the mean test score was highest with 0.859238 when the c and gamma values
were 1000 and 0.01, respectively, thus we selected the c value and the gamma value to be applied to SVM as
1000 and 0.01, respectively.
Table 6. Grid search cross validation results (SVM)
Rank Mean test score c gamma
1 0.859238 1000 0.01
2 0.859177 1 1
3 0.859146 1 0.1

 ISSN: 2088-8708
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3.4. Ensemble model design
Ensemble is a technique that generates a powerful model by combining multiple models to achieve
better prediction performance as compared with using an individual machine learning model. When multiple
models are combined, the amount of calculation is generally increased, yet it prevents overfitting more
effectively than using an individual model and it has the advantage of showing better performance than an
individual model if the performance of an individual model is poor [22-24]. Ensemble is mainly divided into
a collection methodology and a boosting methodology. The collection methodology has the predetermined
set of models to be used, but the boosting methodology gradually increases the models to be used. In this
study, we combined the logistic regression, decision tree, and SVM models previously designed, which
corresponds to the collection methodology, to build an enemble model. Figure 2 shows the structure of the
ensemble model.
Figure 2. Structure of ensemble model
The training set data are used as an input variable to the combined logistic regression, decision tree,
and SVM models, and the predicted results are outputted from an individual model. The final prediction
results are generated by voting on the outputted results [25]. Voting is divided into hard and soft voting. Hard
voting simply selects the final prediction based on the prediction results of an individual model. The voting
method of the ensemble model designed in this study is soft voting, which selects the final prediction based
on the sum of conditional probabilities of an individual model.
4. PERFORMANCE EVALUATION
We evaluated classification performance using the previously configured data set and designed the
classification models. For performance evaluation, we used precision, recall, and f-score based on the error
matrix. Figure 3 shows the error matrices created based on the classification results of the trained models.
Table 7 shows the performance evaluation of the classification models calculated by referring to the error
matrices.
When the logistic regression model predicted 'good', the precision was highest with 0.8685. When
the prediction was performed based on the input data of 'moderate', the recall was highest with 0.9341. On
the other hand, the classification did not work well for ‘bad’ and ‘very bad’. Especially, the prediction was
not made at all for 'very bad'. When the decision tree model predicted 'moderate', the precision was highest
with 0.8977. When the prediction was performed based on the input data of 'moderate', the recall was highest
with 0.9023. On the other hand, the precision and recall for 'bad' and 'very bad' showed relatively low values
compared to 'good' and 'moderate’. The SVM model showed the highest precision and recall with 0.8997 and
0.8997, respectively, for 'moderate’. As in the decision tree model, the precision and recall for 'bad' and 'very
bad' showed relatively low values compared to 'good' and 'moderate’. The ensemble model showed the
highest precision and recall with 0.8997 and 0.8997 for 'moderate’. However, the precision and recall for
'bad' and 'very bad' showed relatively low values compared to 'good' and 'moderate’, resulting in difficulty
classifying the relevant classes. The analysis results based on the precision and recall showed that the
precision and recall of 'good' and 'moderate' were relatively higher than those of 'bad' and 'very bad'. When
analyzed through the error matrixes in Figure 3, it was confirmed that, of the input data, the proportion of

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data corresponding to 'good' and 'moderate' used for classification was high. For an unbalanced model with
the high proportion of a specific class, the generally used accuracy is meaningless. Therefore, we should
evaluate the performance of the model by taking into account all classes with the same proportion, and used
the mean of macro f-score. Other than the logistic regression model with an f-score of 0.4930, the other
models showed the similar scores with 0.8264, 0.8277, and 0.8265.
(a) (b)
(c) (d)
Figure 3. Confusion matrix; (a) Logistic regression model, (b) Decision tree model, (c) SVM model,
(d) Ensemble model
Table 7. Classification performance evaluation by models
Logistic regression Decision tree SVM Ensemble
class precision recall precision recall precision recall precision recall
0 (good) 0.8685 0.8378 0.8616 0.8528 0.8577 0.8578 0.8579 0.8577
1 (moderate) 0.8162 0.9341 0.8977 0.9023 0.8997 0.8997 0.8997 0.8998
2 (bad) 0.6925 0.1510 0.7881 0.7885 0.7885 0.7885 0.7881 0.7885
3 (very bad) 0.0000 0.0000 0.7630 0.7574 0.7647 0.7647 0.7630 0.7574
f-score
(macro)
0.4930 0.8264 0.8277 0.8265
5. CONCLUSION
In predicting particulate matter concentrations, there is a problem of training particulate matter
concentration prediction models because of the characteristics of particulate matter. In order to solve this
problem, various studies have been underway such as performing prediction by dividing particulate matter
concentrations based on a specific concentration. In this paper, to improve the performance of the particulate
matter concentration prediction model, we proposed multiple classification models that provided particulate
matter concentrations in four classes based on the AQI. To this end, we configured data sets by selecting air
pollutant data and meteorological elements collected at an interval of an hour for 10 years around Cheonan.
As the classification models in this study, we used the logistic regression, decision tree, SVM, ensemble. In
order to apply optimal parameters to each model, we searched the parameters through grid search cross
validation. We built the ensemble model by combining the logistic regression, decision tree, and SVM
models into one. We used error matrixes to evaluate the performance of four multiple classification models.
Logistic regression showed poor precision, recall, and f-score compared to other classification models.

 ISSN: 2088-8708
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Decision tree, SVM, and ensemble models all showed the precision and recall with 0.85 or higher for 'good'
and 'moderate' based on the AQI, whereas they showed 0.75~0.79 for '’bad' and 'very bad'. We confirmed
that this was because the particulate matter data used in the classification models were unbalanced data with
the high proportion of a specific class. Accordingly, we verified the scores of the models by taking into
account all classes with the same proportion, and found that the models other than the logistic regression
model showed a score of 0.82 or higher. Of these models, the SVM model showed the best classification
performance with 0.8277. In future, in order to address the problem of unbalanced data, we are going to
compare classification performance through the algorithm changes of the classification models and design a
particulate matter concentration prediction model based on the improved classification models.
ACKNOWLEDGEMENTS
This research was supported by Basic Science Research Program through the National Research
Foundation of Korea (NRF) funded by the Ministry of Education (2019R1I1A3A01059038)
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BIOGRAPHIES OF AUTHORS
Yong-Jin Jung, received his B.S. in Electronics Engineering from Kongju National
University, Cheonan, South Korea, in 2014, and his M.S. in Electrical, Electronics and
Communication Engineering from Korea University of Technology and Education
(KOREATECH) in 2016. He is currently pursuing a Ph.D. in Electrical, Electronics
and Communication Engineering at the Korea University of Technology and Education
(KOREATECH). His research interests include machine learning, data analysis and
deep learning.
Kyoung-Woo Cho, received his B.S. in Electronics Engineering from Kongju
National University, Cheonan, South Korea, in 2013, and his M.S. in Electrical,
Electronics and Communication Engineering from Korea University of Technology
and Education (KOREATECH) in 2015. He is currently pursuing a Ph.D. in Electrical,
Electronics and Communication Engineering at the Korea University of Technology
and Education (KOREATECH). His research interests include machine learning, deep
learning and particulate matters prediction.
Jong-Sung Lee, received his B.S in Information Communication from Korea Nazarene
University, Cheonan, South Korea, in 2011, and his M.S in Electrical, Electronics and
Communication Engineering from Korea University of Technology and Education
(KOREATECH) in 2016. He is currently pursuing a Ph.D. in Electrical, Electronics
and Communication Engineering at the Korea University of Technology and Education
(KOREATECH). His research interests include 5G network, machine learning and
deep learning.
Chang-Heon Oh, received the B.S. and M.S.E. degrees in telecommunication and
information engineering from Korea Aerospace University, Kyunggi-Do, Korea, in
1988 and 1990, respectively. He received the Ph.D. degree in avionics engineering
from Korea Aerospace University, Kyunggi-Do, Korea, in 1996. From February 1990
to August 1993, he was with Hanjin Electronics Co., where he was involved in the
research and development of radio communication & monitoring systems. From
October 1993 to February 1999, he was with the CDMA R&D center of Samsung
Electronics Co., where he was involved in the design and development of CDMA
cellular systems and CDMA PCS systems for successful commercial CDMA
deployment in Korea. Since March 1999, he has been with the School of Electrical,
Electronics and Communication Eng., Korea University of Technology and Education
(KOREATECH), Cheonan, Korea, where he is currently a professor. His research
interest is in the areas of wireless/mobile communication, wireless localization, IoT
and engineering education.

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