Journal of Robotics, Networking and Artificial Life
Vol. 8(4); March (2022), pp. 298–303
DOI: https://doi.org/10.2991/jrnal.k.211108.014; ISSN 2405-9021; eISSN 2352-6386
https://www.atlantis-press.com/journals/jrnal

Research Article

Tuning Suitable Features Selection using Mixed Waste
Classification Accuracy
Hassan Mehmood Khan1, Norrima Mokhtar1,*, Heshalini Rajagopal2, Anis Salwa Mohd Khairuddin1,
Wan Amirul Bin Wan Mohd Mahiyidin1, Noraisyah Mohamed Shah1, Raveendran Paramesran3
Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Malaysia
Department of Electrical and Electronic Engineering, Manipal International University, Malaysia
3
Institute of Computer Science and Digital Innovation, UCSI University, 56000, Kuala Lumpur, Malaysia
1
2

ARTICLE INFOABSTRACT

Article History

Classification accuracy can be used as method to tune suitable features. Some features can be mistakenly selected hence derailed
the classification accuracy. Currently, feature optimization has gained many interests among researchers. Hence, this paper aims
to demonstrate the effects of features reduction and optimization for higher classification results of mixed waste. The most
relevant features with respect to mix waste characteristic were observed with respect to classification accuracy. There are fourstages of features selection. The first stage, 40 features were selected with training accuracy 79.59%. Then, for second stage, better
accuracy was obtained when redundant features were removed which accounted for 20 features with training accuracy of 81.42%.
As for the third stage 17 features were maintained at 90.69% training accuracy. Finally, for the fourth stage, additional two morefeatures were removed, however the classification accuracy was decreased to less than 80%. The experiments results showedthat by observing the classification rate, certain features gave higher accuracy, while the others were redundant. Therefore, inthis study, suitable features gave higher accuracy, on contrary, as the number of features increased, the accuracy rate were notnecessarily higher.

Received 19 October 2020
Accepted 18 October 2021
Keywords
Features reduction
features optimization
higher classification rate
mixed waste classification© 2021 The Authors. Published by Atlantis Press International B.V.
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

1. INTRODUCTION
Solid waste is inextricably related to industrial growth and urbanization. Countries’ economic prosperity rises as they urbanize.
Consumption of goods and services rises in tandem with the levelsof life and disposable incomes, resulting in an increase in waste
generation. According to Karak et al. [1], nearly 1.3 billion tons ofMunicipal Solid Waste (MSW) are produced globally per year, or
1.2 kg each capita per day. The real per capita figures, on the other
hand, are extremely volatile, as waste generation rates vary greatly
across nations, cities, and also within cities. Solid waste is traditionally thought of as a ‘urban’ epidemic. Rural people, on average, are wealthier, consume less store-bought goods, and reuse andrecycle more. As a result, waste production rates in rural areas aremuch smaller. More than half of the world’s population now lives
in cities, and the pace of urbanization is rapidly growing. By 2050[2], as many people will live in cities as the population of the wholeworld in 2000. This will add challenges to waste disposal. People
and companies would almost definitely have to take on greater
responsibility for waste generation and disposal, especially in terms
of product design and waste separation. A stronger focus on ‘urban
extraction’ is also expected to arise, as cities are the largest supplier of resources like metal and paper. Tons of waste are createdevery day, creating a major problem for various cities and municipal authorities due to a lack of landfill capacity to dispose of such
Corresponding author. Email: norrimamokhtar@um.edu.my*

waste. Toxic hazard materials in the waste cause health concerns
as well as environmental harm. Due to a lack of landfill space andenvironmental pollution, as well as its economic effects, recycling
has become a major problem [3].
The methods from neural networks [4] and other classification
methods [5], tree classifier, and quadratic discriminant analysis [3],with optical sensors and controller [6], with image processing software capability [7,8] can provide a promising results for paper andplastic sorting for waste management using artificial intelligence.
Hence, for many artificial intelligence methods, the optimized features need to be identified, to ensure correct classification of themixed waste.
Many interests among researchers published on classification accuracy which discussed on best feature selection [9–11]. Still, to someresearchers, the focus also arises on classification accuracy andcomputational time [12,13]. However, the scope of this work is toshow how the reduction of features can contribute to higher classification accuracy of mixed waste classification.

2. MATERIALS AND METHODS
2.1. Dataset and Training
In this study, 320 sample images were taken for seven different
dry waste garbage classes. The features data was extracted from

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299

greyscale, segmented greyscale images and binarized image of thecaptured RGB images using Matlab2019b. Table 1 dataset used fortraining in each class.

40 features have been selected for the class of interest as shownin Table 2 which extracted from Figure 1 via image processing
method.

2.1.1. Features extraction

2.1.2. Classifiers used for training

From the observation of mixed waste classes which consist of
crumble (paper/plastic), flat (paper/plastic), tin can, bottle (plastic/
glass), cup (paper/plastic), plastic box and paper box, the following
Table 1 | Dataset of classes
Type of mixed waste

DatasetBottleCup
Plastic box
Paper box
Crumble paper/plastic
Flat paper/plastic
Tin can

40
50
40
40
50
50
50

Total dataset

320

Table 3 shows the types of classifiers used with respect to their
specifications. Figure 4 shows the block diagram for training of thedeveloped system.

3. RESULTS AND DISCUSSION
3.1. Features Selections
There are several stages of feature selections carried out from thisstudy. The first stage, all 40 features are used. However, accuracyof the 40 features during training was less than 77.8%. To address
the shortcoming, several tests were conducted and number of
redundant features which contribute less and reduced the percentage rate of the training were removed from the list.

Table 2 | Features list
FeaturesSource image

F1: Round measureof sample images

Greyscale of segmented sample image (Figure 1)

F2: Mid-point
symmetry

Computation of white pixel summation array C.
(a) Segmentation filter. (b) Graph of white pixel
summation array C (Figure 2)

Formula2             4 πA / P 
where, A is the area of the image, P is the perimeter of the image(1)no ∑ a( y ) = 1
y =1n∑ b(z )

* 100 (2)z =1

F3: Skewness

Computation of white pixel summation array C.
(a) Segmentation filter. (b) Graph of white pixel
summation array C (Figure 2)

F4: Mode

Computation of white pixel summation array C.
(a) Segmentation filter. (b) Graph of white pixel
summation array C (Figure 2)
Computation of white pixel summation array C.
(a) Segmentation filter. (b) Graph of white pixel
summation array C (Figure 2)

F5: Kurtosis

where o is the total number of elements and y is the corresponding numberof elements of array a, z is the corresponding elements of array b.
Array a is the first half of the array C, and array b is the second half of thearray C.
   


(3)
s3
where C is the white pixel summation array, E is the expected value ofquantity, m is the mean of C, s is the standard deviation of C.
It returns the mode of C, which is the most frequently occurring value in
C. When there are multiple values occurring equally frequently, mode
returns the smallest of those values.
4             E(C - m ) (4)s4
where C is the white pixel array, E is the expected value of quantity, m is
the mean of C, s is the standard deviation of C.

F6: Mean

Computation of white pixel summation array C.
(a) Segmentation filter. (b) Graph of white pixel
summation array C (Figure 2)            F7: Standard
deviation

Computation of white pixel summation array C.
(a) Segmentation filter. (b) Graph of white pixel
summation array C (Figure 2)         F8: Zero-scores

Computation of white pixel summation array C.
(a) Segmentation filter. (b) Graph of white pixel
summation array C (Figure 2)

3

E(C - m )

1

N

(5)
∑Ci 
N i =1
where C is the white pixel array, N is the observations/values in the array.
N1

å½C - m½ 
N -1
2

i

(6)

i =1

where C is metric of the white pixel summation of the image with N
number of values greater than 0, and m is the mean of C.

éx-m            n êë s

ù> 2ú 

û

(7)

where n is the total numbers of absolute value array, m is the mean ands is the standard deviation of the C.
(Continued)

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300

Table 2 | Features list­—Continued
FeaturesSource image

FormulaF9: Quantile

Computation of white pixel summation array C.
(a) Segmentation filter. (b) Graph of white pixel
summation array C (Figure 2)
Computation of white pixel summation array C.
(a) Segmentation filter. (b) Graph of white pixel
summation array C (Figure 2)          where p = 25

F10: Derivative
variance

F11: Standard
deviation

Greyscale of segmented sample image (Figure 3)

max(quantile(C , p)) 

(8)

1 N
2
Ci − m            ∑N − 1 i =1
where C is metric of the white pixel summations of the image with N
number of values greater than 0, and m is the mean of C.
          

N

1∑C −m 
N −1
2

i

(9)

(10)

i =1

where C is metric of the greyscale of the segmented image with N numberof values greater than 0, and m is the mean of C.

F12: Entropy

Greyscale of segmented sample image (Figure 3)
Entropy of the segmented grey image.

F13: Greyscalelevel ratio range(>40 and <111)
F14: Greyscalelevel ratio range(>110 and <181)
F15: Greyscalelevel ratio range(>180 and <=255)
F16: Grey-levelco-occurrence
matrix (Contrast)

Greyscale of segmented sample image (Figure 3)

Greyscale of segmented sample image (Figure 3)
Measure of the intensity contrast between a pixel
and its neighbor over the whole image.

          å i - j p(i , j) F17: Grey-levelco-occurrence
matrix (Correlation)

Greyscale of segmented sample image (Figure 3)
Measure of correlated pixel to its neighborover the whole image.

        åF18: Grey-levelco-occurrence
matrix (Energy)

Greyscale of segmented sample image (Figure 3)
Returns the sum of squared elements in
the GLCM.

Greyscale of segmented sample image (Figure 3)

Percentage of grey pixels having intensity greater than 110 and less than
181 in the segmented grey image pixel array.

Greyscale of segmented sample image (Figure 3)

Percentage of grey pixels having intensity greater than 180 and less than orequal to 255 in the segmented grey image pixel array.

F19: Grey-level
Greyscale of segmented sample image (Figure 3)
co-occurrence matrix Returns a value that measures the closeness ofthe distribution of elements in the GLCM to(Homogeneity)
the GLCM diagonal.
F20: Entropy

F21–F40           -sum( p.*log 2( p)) (11)where p contains the normalized histogram counts.
Percentage of grey pixels having intensity greater than 40 and less than
111 in the segmented grey image pixel array.

Binarized image of segmentation filter(Figure 2a)
Entropy of binary of the segmented imageof the samples.
Mean of Gabor filters which were created fromsegmented grey image, with four different
frequencies f, and six different wavelengths l.

2

(12)

i, j

where p contains the normalized histogram counts. (i, j) are position
values of pixels.
(i - m i )( j - m j)(i , j)s is iwhere (i, j) position values of pixels. m is the mean of the pixel at theposition (i, j). s is standard deviation of pixel at the position (i, j).

(13)          ∑ p(i , j) 

(14)

i, j

2

i, j

where p contains the normalized histogram counts. (i, j) are position
values of pixels.
         åp(i , j)
(15)
1+ i - jwhere p contains the normalized histogram counts. (i, j) are position
values of pixels.
i, j        −sum(p.*log 2( p)) 
where p contains the normalized histogram counts.

(16)

Source image: Greyscale of segmented sample image (Figure 3)
f = [0, 90, 135, 45]l = [2.8, 5.65, 11.314, 22.63, 45.25, 90.51]

GLCM, Grey-level co-occurrence matrix.

In the second stage, 20 features were selected from the 40 features.
After many features tuning and combination, it was observed that
F1–F5, F7, F8, F10, F14, F30–F35, F37–F40 were insignificant anddid not contribute to the classification process. These features were
removed which then contributed to mean of 79.12%

Figure 1 | Segmentation stages for bottle class sample.

In the third stage, only 17 features were used which consists of F9,
F11–F13, F15–F24, F26–F28. For these 17 features, Q.SVM andC.SVM showed accuracy above 88%.

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a

b

301

3.2. Classifiers Training Results
The training accuracy for respective classifiers with respect to 40, 20,
17 and 15 features are shown in Table 4. For Q.SVM, C.SVM, Fine
KNN, E. Boosted Trees and E. Bagged Trees have consistent higher
accuracy for 17 features. The results verified that the 17 features gave
good characteristic of the waste, therefore proves that good features
will always provide higher accuracy. The best performance classifier
with 17 features was Q.SVM which has accuracy of 90.69%.

Figure 2 | Computation of white pixel summation array C.
(a) Segmentation filter. (b) Graph of white pixel summation array C.

3.3. Classification Results of Mixed Waste
Classification
Based on the training performance which discussed in Section 3.2,
Q.SVM has the highest accuracy of the optimized 17 features. With
that confirmation, testing was done using 320 images of mixed
waste which consist of crumble (paper/plastic), flat (paper/plastic), tin can, bottle (plastic/glass), cup (paper/plastic), plastic box
and paper box. Table 5 shows the summary of the testing accuracy

Figure 3 | Greyscale of segmented image.
Figure 5 | Summary of features selections with respect to each stage.
Table 3 | Classifiers and specifications
No

ClassifiersSpecifications

1

Support Vector Machine –Cubic (C.SVM)
Support Vector Machine –Quadratic (Q.SVM)
Ensemble Bagged Trees(E. Bagged Trees)
k-Nearest Neighbor(Fine.kNN)
Ensemble Boosted Trees(E. Boosted Trees)

Kernel function: Cubic, k = 1,
One-vs-one method
Kernel function: Cubic, k = 1,
One-vs-one method
Maximum no of splits = 342, no oflearners = 30, learning rate = 0.1
k = 102
3
4
5

Maximum no of splits = 20, no oflearners = 30, learning rate = 0.1

Table 4 | Training accuracy for multiple classifiers for 40, 20, 17 and 15
features
Number of features
Classifiers

40

20

17

15

Classification accuracy
Q.SVM (%)
C.SVM (%)
Fine.KNN (%)
E. Boosted Trees (%)
E. Bagged Trees (%)
Mean (%)

78.97
79.59
73.31
78.15
79.01
77.80

90.69
88.77
83.92
86.49
81.21
86.22

81.42
81.27
76.25
77.52
79.13
79.12

81.83
81.69
77.6
77.61
77.55
79.27

Table 5 | Training accuracy for Q.SVM classifiers for 40, 20, 17 and 15
features
Number of features
Type of waste

40

20

17

15

Q.SVM classification accuracy
Figure 4 | Flowchart for classifier training.

In the fourth stage, F9 and F11 were removed. F12, F13, F15–F24,
F26–F28 were maintained. However, the accuracy was severely
impacted with average results lest than 80%. Figure 5 summed up
the performance with respect to features selections stages.

Crumble (paper/plastic) (%)
Flat (paper/plastic) (%)
Tin can (%)
Bottle (plastic/glass) (%)
Cup (paper/plastic) (%)
Plastic box (%)
Paper box (%)
Mean (%)

85
75
66
71
69
80
85
75.86

100
89
95
59
71
79
70
80.43

100
100
94
76
90
85
84
89.86

83
74
70
71
72
82
86
76.86

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of Q.SVM classifier which averaged to 89.9% for 17 features. Allclasses have higher accuracy except for bottle (plastic/glass).

4. CONCLUSION
From this study, it was observed that relevant features and accuracy are good matched to ensure the performance of mixed waste
classification. The relevant features were studied stage by stage. In
the first stage, even though more features were assigned, the classifiers training showed poor performance. In the second stage, after
reduction of features, by experimenting the features and training
rate, the redundant features which contributed to low accuracy
have been removed. Eventually the classification rate of training
increased to average of 79.12%. Further reduction to 17 features,
have contributed to higher average accuracy of 86.22%. However,further reduction did not contribute to better classification rate.
The selected 17 features with Q.SVM as classifier also proved withhigher accuracy for training with 90.69% and testing with 89.9%. In
a nutshell, the important of the correct features selection is crucial
for mix waste classification. Optimal features selection, proved forhigher accuracy of mixed waste classification. On contrary, morefeatures not necessarily contributed to higher accuracy.

CONFLICTS OF INTEREST
The authors declare they have no conflicts of interest.

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AUTHORS INTRODUCTION
Mr. Hassan Mehmood Khan

Dr. Norrima Mokhtar

He received his B.E. in Electronics from Dawood
University of Engineering and Technology,
Pakistan in 2009. He is currently submitted his
Master of Engineering Science with Universityof Malaya, Malaysia. From 2015 to 2020, he has
been as Head of Department (Mechatronics) at
MIT Academy Sdn. Bhd. His research interests include automation, image processing, machine learning, and artificial
intelligence.

She received the B. Eng. degree in ElectricalEngineering from University of Malaya in 2000.
After working two years with the International
Telecommunication Industry with attachment
at Echo Broadband GmbH, she managed tosecure a Panasonic Scholarship which required
intensive screening at the national level in 2002. She finished
her Master of Engineering (Oita, Japan) under financial support
received from Panasonic Scholarship in 2006. Consequently,
she was appointed as a lecturer to serve the Department ofElectrical Engineering, University of Malaya immediately after
graduating with her Master of Engineering. As part of her careerdevelopment, she received a SLAB/SLAI scholarship to attainher PhD in 2012. Her research interests are signal processing
and human machine interface.

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303

Dr. Heshalini Rajagopal

Dr. Noraisyah Binti Mohamed Shah

She received her Master’s degree from the
Department of Electrical Engineering,
University of Malaya, Malaysia in 2016. She
has passed her PhD Viva Voce in Universityof Malaya, Malaysia recently. She received the
B.E. (Electrical) in 2013 and M. Eng. Sc. in 2016
from University of Malaya, Malaysia. Currently, she is a lecturer in Manipal International University, Nilai, Malaysia. Her
research interest include image processing, artificial intelligenceand machine learning.

She received the B. Eng. degree from Universityof Malaya in 1999, Master Degree from Oita
Universitym, Japan, and the PhD degree from
George Mason University. She is currently asenior lecturer at the Department of ElectricalEngineering, University of Malaya. Her researchinterests are image processing, machine learning and satellite
communication.

Prof. Dr. Raveendran ParamesranDr. Anis Salwa Mohd Khairuddin
She received PhD in computer engineering at Universiti Teknologi Malaysia (UTM).
She received the B. Eng (Hons) Electrical andElectronics Engineering from Universiti Tenaga
Nasional, Malaysia, in 2007 and Masters degree
in computer engineering from Royal Melbourne
Institute of Technology, Australia, in 2009. She is currently a
Senior Lecturer from University of Malaya. Her current interests include pattern recognition, image analysis, and artificial
intelligence.

Dr. Wan Amirul Bin Wan Mohd Mahiyidin
He received the M. Eng. degree from theImperial College London in 2009, the M.Sc.
degree from University of Malaya in 2012, and
the PhD degree from University of Canterbury
in 2016. He is currently a senior lecturer at
the Department of Electrical Engineering,
University of Malaya. His research interests are multiple antennas system, cooperative MIMO, channel modelling and positioning system.

He (SM’01) received the B.Sc. and M.Sc. degrees
in electrical engineering from South Dakota
State University, Brookings, SD, USA, in 1984
and 1985, respectively, and the PhD degree fromthe University of Tokushima, Japan, in 1994. He
was a Systems Designer with Daktronics, USA.
He joined the Department of Electrical Engineering, Universityof Malaya, Kuala Lumpur, in 1986, as a Lecturer. In 1992, hereceived a Ronpaku Scholarship from Japan. He was promoted
to Associate Professor in 1995, then was promoted to Professor
in 2003. His research areas include image and video analysis,
formulation of new image descriptors for image analysis, fastcomputation of orthogonal moments, analysis of EEG signals,
and data modeling of substance concentration acquired from
non-invasive methods. His contributions can be seen in theform of journal publications, conference proceedings, chapters in books and an international patent to predict blood glucose levels using non-parametric model. He has successfully
supervised to completion 14 PhD students and 12 students in
M. Eng. Sc. (Masters by research). He is currently a member of
the Signal Processing Society.