A FUZZY INTERACTIVE BI-OBJECTIVE MODEL FOR SVM TO IDENTIFY THE BEST COMPROMISE SOLUTION FOR MEASURING THE DEGREE OF INFECTION WITH CORONA VIRUS DISEASE (COVID-19)

International Journal of Fuzzy Logic Systems (IJFLS) Vol.11, No.1, January 2021
DOI : 10.5121/ijfls.2021.11102 13
A FUZZY INTERACTIVE BI-OBJECTIVE MODEL FOR
SVM TO IDENTIFY THE BEST COMPROMISE
SOLUTION FOR MEASURING THE DEGREE OF
INFECTION WITH CORONA VIRUS
DISEASE (COVID-19)
Mohammed Zakaria Moustafa1
, Hassan Mahmoud Elragal2
,
Mohammed Rizk Mohammed2
, Hatem Awad Khater3
and Hager Ali Yahia2
1
Department of Electrical Engineering (Power and Machines Section)
ALEXANDRIA University, Alexandria, Egypt
2
Department of Communication and Electronics Engineering,
ALEXANDRIA University, Alexandria, Egypt
3
Department of Mechatronics, Faculty of Engineering,
Horus University, Egypt
ABSTRACT
A support vector machine (SVM) learns the decision surface from two different classes of the input points.
In several applications, some of the input points are misclassified and each is not fully allocated to either
of these two groups. In this paper a bi-objective quadratic programming model with fuzzy parameters is
utilized and different feature quality measures are optimized simultaneously. An α-cut is defined to
transform the fuzzy model to a family of classical bi-objective quadratic programming problems. The
weighting method is used to optimize each of these problems. For the proposed fuzzy bi-objective quadratic
programming model, a major contribution will be added by obtaining different effective support vectors
due to changes in weighting values. The experimental results, show the effectiveness of the α-cut with the
weighting parameters on reducing the misclassification between two classes of the input points. An
interactive procedure will be added to identify the best compromise solution from the generated efficient
solutions. The main contribution of this paper includes constructing a utility function for measuring the
degree of infection with coronavirus disease (COVID-19).
KEYWORDS
Support vector machine (SVMs); Classification; Multi-objective problems; Weighting method; fuzzy
mathematics; Quadratic programming; Interactive approach; COVID-19.
1. INTRODUCTION
Nowadays, the coronavirus spread between people all over the world, every day the number of
infected people is increased. These diseases can infect both humans and animals. So, the
detection of coronavirus (COVID-19) is now a critical task for the medical practitioner. In this
paper, the support vector machine is suggested for detection of coronavirus infected patient using
different features (X-ray images, Fever, Cough and Shortness of breath), and help the decision
makers to determine the number of patients who must be isolated according to the degree of
infection.

14
Support Vector Machines (SVMs) are a classification technique developed by Vapnik at the end
of ’60s [1]. The theory of support vector machines (SVMs) is a new classification technique and
has drawn much attention on this topic in recent years [6]. Since then the technique has been
deeply improved, being applied in many different contexts.
In many applications, SVM has been shown to provide higher performance than traditional
learning machines [6]. SVMs are known as maximum margin classifiers, since they find the
optimal hyperplane between two classes as shown in figure1, defined by a number of support
vectors [4].
Figure 1. Maximization of the margin between two classes
The well-known generalization feature of the technique is mainly due to the introduction of a
penalty factor, named C that allows us to prevent the effects of outliers by permitting a certain
amount of misclassification errors.
In this paper, the idea is to apply the fuzzy multi-objective programming technique for
developing the set of all efficient solutions for the classification problem with minimum errors.
An α-cut is taken to transform the fuzzy multi-objective problem model to a classical one (α
problem). The weighting method is used to solve the α problem proposed to generate the set of
efficient solutions for the proposed model. The remainder of this paper is organized as follows. A
brief review for the SVM is described in section 2. The proposed fuzzy bi-objective model for the
Support Vector Machine will be derived in section 3. NEXT, section 4 presents three numerical
examples corresponding to three different α-cut. Section 5 provides our general conclusions.
2. SUPPORT VECTOR MACHINES
SVM is an efficient classifier to classify two different sets of observations into their relevant class
as shown in figure 2 where there are more than straight line separates between the two sets. SVM
mechanism is based upon finding the best hyperplane that separates the data of two different
classes of the category. The best hyperplane is the one that maximizes the margin, i.e., the
distance from the nearest training points [2].

15
Support vector machine has been utilized in many applications such as biometrics,
chemoinformatics, and agriculture. SVM has penalty parameters, and kernel parameters that have
a great influence on the performance of SVM [3]. We review the basis of the theory of SVM in
classification problems [7].
Let a set S of labelled training points
(𝑦1,𝑥1)… (𝑦𝑙,𝑥𝑙). (1)
Where,xi ∈ ℛN
belongs to either of two classes and is given a labelyi = {−1,1} for i = 1, … , l.
Figure 2. Data classification using support vector machine
In some cases, to get the suitable hyperplane in an input space, mapping the input space into a
higher dimension feature space and searching the optimal hyperplane in this feature space.
Let z = 𝜑(𝑥) denote the corresponding feature space vector with mapping 𝜑 from ℛ𝑁
to a feature
space ᵶ. We wish to find the hyperplane
𝑤. 𝑧 + 𝑏 = 0 (2)
defined by the pair (w, b) according to the function
𝑓(𝑥𝑖) = 𝑠𝑖𝑔𝑛(𝑤. 𝑧𝑖 + 𝑏) = {
1, 𝑖𝑓𝑦𝑖 = 1
−1, 𝑖𝑓𝑦𝑖 = −1
(3)
where w ∈ ᵶ and b ∈ ℛ. For more precisely the equation will be
{
(𝑤. 𝑧𝑖 + 𝑏) ≥ 1, 𝑖𝑓𝑦𝑖 = 1
(𝑤. 𝑧𝑖 + 𝑏) ≤ −1, 𝑖𝑓𝑦𝑖 = −1,
𝑖 = 1, … , 𝑙 (4)
For the linearly separable set S, we can find a unique optimal hyperplane for which the margin
between the projections of the training points of two different classes is maximized.
For the data that are not linearly separable figure 3, the previous analysis can be generalized by
introducing some nonnegative variables ξ𝑖
≥ 0 then,
𝑦𝑖(𝑤. 𝑧𝑖 + 𝑏) ≥ 1 − ξ𝑖
, 𝑖 = 1, … , 𝑙. (5)

16
The term ∑ ξ𝑖
𝑙
𝑖=1 can be thought of as some measure of the amount of misclassifications.
The optimal hyperplane problem is then regarded as the solution to the problem
𝑚𝑖𝑛𝑖𝑚𝑖𝑧𝑒
1
2
𝑤. 𝑤 + 𝐶 ∑ ξ𝑖
𝑙
𝑖=1
𝑠𝑢𝑏𝑗𝑒𝑐𝑡𝑡𝑜𝑦𝑖(𝑤. 𝑧𝑖 + 𝑏) ≥ 1 − ξ𝑖
, (6)
𝑖 = 1, … , 𝑙
ξ𝑖
≥ 0, 𝑖 = 1, … , 𝑙
where, 𝐶 is a constant. The parameter 𝐶 can be regarded as a regularization parameter [5]. SVM
algorithms use a set of mathematical functions that are defined as the kernel.
The function of kernel is to take data as input and transform it into the required form. Different
SVM algorithms use different types of kernel functions. For example, linear, nonlinear,
polynomial, radial basis function (RBF), and sigmoid.
Basically, the training part consists in finding the best separating plane (with maximal margin)
based on specific vector called support vector. If the decision is not feasible in the initial
description space, you can increase space dimension thanks to kernel functions and may be find a
hyperplane that will be your decision separator.
Figure 3. Linearly separable and nonlinearly separable
3. FORMULATION OF THE FUZZY BI-OBJECTIVE QUADRATIC
PROGRAMMING MODEL OF SVM
In this section, we make a detail description about the idea and formulation of the fuzzy bi
objective programming model for the SVM. SVM is a powerful tool for solving classification
problems, but due to the nonlinearity separable in some of the input data, there is an error in
measuring the amount of misclassification. In the same time, in many real-world applications,
each of the input points does not exactly belong to one of the two classes [11].
From this point of view, we reformulate the classical model of the SVM to the following bi-
objective programming model with fuzzy parameters.

17
3.1. The Fuzzy Bi-Objective Support Vector Machine (FSVM):
Now, we add another objective function with fuzzy parameters ῦ𝑖,𝑖 = 1,2, … , 𝑙 for the previous
model in section 2 to be in the form [14]
Min ∥ 𝑤 ∥2
,
Min ∑ ῦ𝑖 ξ𝑖
𝑙
𝑖=1
Subject to (7)
𝑦𝑖(𝑤. 𝑥𝑖 + 𝑏) ≥ 1 + ξ𝑖
, 𝑖 = 1,2, … , 𝑙
ξ𝑖
≥ 0 , 𝑖 = 1,2, … , 𝑙
By taken an α-cut for the membership functions corresponding to the fuzzy parameters ῦ𝑖, 𝑖 =
1,2, … , 𝑙, we get the following α-problem:
Min ∥ 𝑤 ∥2
,
Min ∑ α𝑖ξ𝑖
𝑙
𝑖=1
Subject to (8)
𝑦𝑖(𝑤. 𝑥𝑖 + 𝑏) ≥ 1 + ξ𝑖
, 𝑖 = 1,2, … , 𝑙
ξ𝑖
≥ 0 , 𝑖 = 1,2, … , 𝑙
σ ≤ α𝑖 ≤ 1 , 𝑖 = 1,2, … , 𝑙
With sufficient small σ > 0.
Where the parameter ξ𝑖
is a measure of the error in the SVM and the term α𝑖ξ𝑖
is a measure of
the error with different degrees α𝑖. The (α-problem) is solved by the weighting method to get the
set of all efficient solutions.
This problem is a bi-objective quadratic programming problem. The first objective is to
maximize the gap between the two hyperplanes which used to classify the input points. The
second objective is to minimize the error (with different degrees α𝑖 , 𝑖 = 1,2, … , 𝑙) in measuring
the amount of misclassification in case of nonlinearity separable input points [11].
Problem 8 can be solved by the weighting method to get the set of all efficient solutions for the
classification problem.
The right choice of weightage for each of these objectives is critical to the quality of the classifier
learned, especially in case of the class imbalanced data sets. Therefore, costly parameter tuning
has to be undertaken to find a set of suitable relative weights [10].
3.2. The Weighting Method
In this method each objective 𝑓𝑖(𝑋), 𝑖 = 1,2, … , 𝑘, is multiplied by a scalar weigh𝑤𝑖 ≥
0 𝑎𝑛𝑑 ∑ 𝑤𝑖 = 1.
𝑘
𝑖=1 Then, the k weighted objectives are summed to form a weighted-sums
objective function [8][12].
𝐴𝑠𝑠𝑢𝑚𝑒 𝑊 as {
𝑤 ∈ 𝑅𝑘
: 𝑤𝑖 ≥ 0,
𝑖 = 1,2, … , 𝑘
𝑎𝑛𝑑 ∑ 𝑤𝑖 = 1
𝑘
𝑖=1
}(9)
be the set of nonnegative weights. Then the weighting problem is defined as:

18
𝑃(𝑊): 𝑀𝑖𝑛 ∑ 𝑤𝑖𝑓𝑖
𝑘
𝑖=1
Subject to 𝑀 = {
𝑋 ∈ 𝑅𝑛
: 𝑔𝑟(𝑋) ≤ 0,
𝑟 = 1,2, … , 𝑚
}. (10)
Then, in this paper the weighting method takes the form
Inf z =𝑤1 ∥ 𝑤 ∥2
+ 𝑤2 ∑ α𝑖ξ𝑖
𝑙
𝑖=1
Subject to
𝑦𝑖(𝑤. 𝑥𝑖 + 𝑏) ≥ 1 + ξ𝑖
, 𝑖 = 1,2, … , 𝑙
ξ𝑖
≥ 0 , 𝑖 = 1,2, … , 𝑙 (11)
𝑤1 > 0, 𝑤2 ≥ 0
𝑤1 + 𝑤2 = 1
σ ≤ α𝑖 ≤ 1 , 𝑖 = 1,2, … , 𝑙
With sufficient small σ > 0
Here we use “Inf “instead of “Min” since the set of constraints is unbounded, where 𝑤1 ≠ 0.
Also, we avoid the redundant solutions by adding the constraint 𝑤1 + 𝑤2 = 1.
3.3. An Interactive Procedure to Identify the Best Compromise Solution
For the version of our bi-objective (SVM) model which applies to determine the best compromise
solution, we need the following hypothesis (after the interaction with the decision maker) [13]:
The best compromise solution from the set of the generated efficient solutions is that efficient one
corresponding to
min
α
𝑁+
≤ min
α
𝑁−
Where, 𝑁−
is the number of support vectors of the negative class,
𝑁+
is the number of support vectors of the positive class.
We must notice that this hypothesis can be reversed according to the preference of the decision
maker (see Yaochu Jin,2006) [9].
Considering the utility function u(xi) is the distance between xi, i=1,2,3,…,nand the best
compromise hyperplane, then we can construct the membership degree of each xi belonging to its
+ve or -ve class as follow:
degree of 𝑥𝑖
+
= 𝜇(𝑥𝑖
+) =
𝑢(𝑥𝑖
+)
𝑀𝑎𝑥𝑖=1
𝑚 𝑢(𝑥𝑖
+)
∈ [0 1]
Similarly, degree of 𝑥𝑖
−
= 𝜇(𝑥𝑖
−) =
𝑢(𝑥𝑖
−)
𝑀𝑎𝑥𝑖=1
𝑙 𝑢(𝑥𝑖
−)
∈ [0 1], l + m = n (11)
For our problem of corona virus disease, the decision maker can take a threshold level 𝜆+
(𝑜𝑟 𝜆−
)
to isolate ones with 𝜇(𝑥𝑖
+) ≥ 𝜆+
, or to be in the safe side, he can decide to isolate ones with
𝜇(𝑥𝑖
−) ≤ 𝜆−
, that is according to his preferences.
The following figure shows how to predict the number of the infected people by using SVM.

19
Figure 4. detection No. of infected people by SVM
4. NUMERICAL EXAMPLES
By using python program, we can solve the previous problem and show the effect of different
values of the weighting parameters. The data set that is used in these examples consist of 51
points and each point has two features, table 1 shows part of this data.
Table 1. Description of part of datasets used in our study.
X1 X2 Y
1.9643 4.5957 1
2.2753 3.8589 1
2.9781 4.5651 1
2.932 3.5519 1
3.5772 2.856 1
0.9044 3.0198 0
0.76615 2.5899 0
0.086405 4.1045 0
Figure 5. 𝑤2 =
1
2
, 𝑤1 =
1
2
, α=
1
4
, number of support vectors = 20

20
Figure 6. 𝑤2 =
1
2
, 𝑤1 =
1
2
, α=
1
2
, number of support vectors = 16
Figure 7. 𝑤2 =
1
2
, 𝑤1 =
1
2
, α=1, number of support vectors = 12
So, the previous results, by using different degrees (α) at the same weights (𝑤1 & 𝑤2), show how
these parameters (α, 𝑤1, 𝑤2) effect on the performance of SVM. When the value of α is increased
the number of support vectors is reduced.
There are good reasons to prefer SVMs with few support vectors (SVs). In the hard-margin case,
the number of SVs (#SV) is an upper bound on the expected number of errors made by the leave-
one-out procedure [9].
So, we can control the performance of SVM according to our requirements by adjusting the
values of the parameters (α, 𝑤1, 𝑤2).
According to our hypothesis that presented in section 3.3, the best compromise solution is that
corresponding to α=1.

21
Table 2. Description of membership degree of some points used in our study
If the decision maker put 𝜆+
= 0.3, then he will isolate ones that are corresponding
to(𝑥1
+
, 𝑥3
+
, 𝑥4
+
, 𝑥6
+
, 𝑥7
+
, 𝑥8
+
,𝑥9
+
, 𝑥10
+
, 𝑥11
+
, 𝑥13
+
, 𝑥14
+
, 𝑥16
+
, 𝑥17
+
, 𝑥18
+
, 𝑥19
+
, 𝑥50
+
, 𝑥51
+
), it will be 15 patients of
51. But if he wants to be in the safe side, he will put 𝜆−
=0.1, then he will isolate ones that are
correspondingto the positive side
(𝑥1
+
, 𝑥2
+
, 𝑥3
+
, 𝑥4
+
, 𝑥5
+
,𝑥6
+
, 𝑥7
+
, 𝑥8
+
, 𝑥9
+
, 𝑥10
+
, 𝑥11
+
, 𝑥12
+
, 𝑥13
+
, 𝑥14
+
, 𝑥15
+
, 𝑥16
+
, 𝑥17
+
, 𝑥18
+
, 𝑥19
+
, 𝑥20
+
, 𝑥50
+
, 𝑥51
+
), it
will be 22 patients of 51.
We think that is a simple decision rule for any decision make in case of infection diseases like the
coronavirus disease(COVID-19).
5. CONCLUSIONS
This paper introduced the fuzzy multi-objective programming technique for developing the set of
all efficient solutions for the classification problem with minimum errors. The weighting method
is used to solve our fuzzy model after defuzzification by using the α − cut technique. The
experimental evaluation was carried out using 51 datasets, each one has two features. The
𝑥𝑖
+
𝜇(𝑥𝑖
+
)
𝑥1
+ 0.55
𝑥2
+
0.275
𝑥3
+
0.875
𝑥4
+
0.325
𝑥5
+ 0.175
𝑥6
+
0.525
𝑥7
+
0.425
𝑥8
+
1
𝑥9
+ 0.55
𝑥10
+
0.775
𝑥11
+
0.675
𝑥12
+
0.125
𝑥13
+ 0.25
𝑥14
+
0.55
𝑥15
+
0.2
𝑥16
+
0.8
𝑥17
+ 0.75
𝑥18
+
0.625
𝑥19
+
0.475
𝑥20
+
0.25
𝑥50
− 0.925
𝑥51
−
0.375
𝑥𝑖
−
𝜇(𝑥𝑖
−
)
𝑥21
−
0.25
𝑥22
−
0.175
𝑥23
−
0.425
𝑥24
−
0.35
𝑥25
−
0.175
𝑥26
− 0.175
𝑥27
− 0.3
𝑥28
− 0.625
𝑥29
− 0.425
𝑥30
−
0.8
𝑥𝑖31
−
0.625
𝑥32
−
0.925
𝑥33
−
0.375
𝑥34
−
0.375
𝑥35
−
0.6
𝑥36
−
0.7
𝑥37
−
0.975
𝑥38
−
0.8
𝑥39
−
0.6
𝑥40
−
0.725
𝑥41
−
0.4
𝑥42
−
0.45
𝑥43
−
0.25
𝑥44
−
0.625
𝑥45
−
0.325
𝑥46
− 0.525
𝑥47
−
1
𝑥48
− 0.15
𝑥49
−
0.65

22
experimental results show the effect of the parameters (α, 𝑤1, 𝑤2) on the misclassification
between two sets. An interactive hypothesis is added to identify the best compromise hyperplane
from the generated efficient set.
Finally, a utility function is constructed to select the best compromised hyperplane from the
generated set of the efficient solutions and it is used to measure the degree of infection with
coronavirus disease (COVID-19) to help the decision maker to detect the number of patients who
must be isolated.
Our future work is to apply our model for large scale.
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A FUZZY INTERACTIVE BI-OBJECTIVE MODEL FOR SVM TO IDENTIFY THE BEST COMPROMISE SOLUTION FOR MEASURING THE DEGREE OF INFECTION WITH CORONA VIRUS DISEASE (COVID-19)

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