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Copyright ⓒ 2018 GiRI (Global IT Research Institute)Dr. Youssef SAID, Tunisie Telecom, Tunisia
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Copyright ⓒ 2018 GiRI (Global IT Research Institute)Dr. Che-Sheng Chiu, Chunghwa Telecom Laboratories, Taiwan
Dr. Chirawat Kotchasarn, RMUTT, Thailand
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Dr. Kamrul Hasan Talukder, Khulna University, Bangladesh
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Prof. Juinn-Horng Deng, Yuan Ze University, Taiwan
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Prof. Junhui Zhao, Beijing Jiaotong University, China
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Prof. Davar PISHVA, Ph.D., Asia Pacific University, Japan
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Prof. Amirmansour Nabavinejad, Ph.D., Sepahan Institute of Higher Education, Iran

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Copyright ⓒ 2018 GiRI (Global IT Research Institute)Volume. 7 Issue. 3

6J

1 A Supervised Machine Learning Approach using Different Feature Selection Techniques on Voice
Datasets for Prediction of Parkinson’s Disease

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Satyabrata Aich*, Hee-Cheol Kim*, Kim younga**, Kueh Lee Hui***, Ahmed Abdulhakim Al-Absi**** and Mangal Sain******Department of Computer Engineering, Inje University, South Korea*Department of Rehabilitation Science, Inje University, South Korea***Department of Electrical Engineering, Dong-A University, South Korea****Department of Computer Engineering, Kyungdong University- Global Campus, Gangwondo, South Korea*****Department of Computer Engineering, Dongseo University, South Korea

2 Prediction of Quality for Different Type of Wine based on Different Feature Sets Using Supervised
Machine Learning Techniques

1122

Satyabrata Aich*, Ahmed Abdulhakim Al-Absi**, Kueh Lee Hui***, and Mangal Sain*****Department of Computer Engineering, Inje University, South Korea**Department of Computer Engineering, Kyungdong University- Global Campus, Gangwondo, South Korea***Dept. of Electrical Engineering, Dong-A University, South Korea****Department of Computer Engineering, Dongseo University, South Korea

Copyright ⓒ 2018 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 7, Issue 3, May 2018

1116

A Supervised Machine Learning Approach using
Different Feature Selection Techniques on Voice
Datasets for Prediction of Parkinson’s Disease
Satyabrata Aich*, Hee-Cheol Kim*, Kim younga**, Kueh Lee Hui***, Ahmed Abdulhakim Al-Absi**** and Mangal
Sain******

Department of Computer Engineering, Inje University, South Korea
Department of Rehabilitation Science, Inje University, South Korea****

Department of Electrical Engineering, Dong-A University, South Korea
Department of Computer Engineering, Kyungdong University- Global Campus, Gangwondo, South Korea*****
Department of Computer Engineering, Dongseo University, South Korea
satyabrataaich@gmail.com, heeki@inje.ac.kr, kya2664@hanmail.net, leehkueh@dau.ac.kr, absiahmed@kduniv.ac.kr,
mangalsain1@gmail.com****

Abstract—Among the neurological diseases, parkinson’s diseaseis the second most common disease, which affect the old age
people over the age of 65 year. It is also mentioned that the
number of people affected with Parkinson’s disease will increaseat a higher rate until 2050, and it will be a rising concern to many
developed countries because the cost due to the healthcare
service of these disease is really high. Parkinson’s disease (PD)belongs to the group of neurological disorder, which directly
affect the brain cells and the effect is shown in terms of
movement, voice and other cognitive disabilities. Past few years
researchers are working for detection and monitoring of the
Parkinson’s disease by using the speech analysis as well as thegait analysis data. Machine learning and artificial intelligence
techniques are gaining popularity because these techniques areable to automate the pattern recognition process with highaccuracy.

———————————————————————
Manuscript received on January 2, 2018. This work was supported by
Kyungdong University grant, 2018 and a follow-up the invited journal to theoutstanding paper of the 20th International Conference on Advanced
Communication Technology (ICACT 2018).
Satyabrata Aich is with the Department of Computer Engineering, Inje
University, South Korea (e-mail: satyabrataaich@gmail.com)
Hee-Cheol Kim is with the Department of Computer Engineering, Inje
University, South Korea (e-mail: heeki@inje.ac.kr)
Kim younga is with the Department of Rehabilitation Science, Inje
University, South Korea (e-mail: kya2664@hanmail.net)
Kueh Lee Hui is with the Department of Electrical Engineering, Dong-A
University, South Korea (e-mail: leehkueh@dau.ac.kr)
Ahmed Abdulhakim Al-Absi is with the Department of Computer
Engineering, Kyungdong University- Global Campus, Gangwondo, South
Korea (e-mail: absiahmed@kduniv.ac.kr)
Mangal Sain is with the Department of Computer Engineering, Dongseo
University, South Korea. He is the corresponding author of this paper.
(Corresponding
authorphone:+8251-320-2009;e-mail:mangalsain1@gmail.com).

However so far, no body has compared the performance
metrics using different feature sets by applying nonlinear andlinear classification approaches based on the voice data. So, in
this paper we have proposed a new approach by comparing theperformance metrics with different feature sets such as genetic
algorithm-based feature sets as well as Principal Component
Analysis based feature reduction technique for selecting thefeature sets. We have used different classification approaches tocompare the performance metrics. We have found an accuracy of
97.57% using SVM with RBF by using genetic algorithm-based
feature sets. This analysis will help the clinicians to differentiate
the PD group from healthy group based on the voice data.
Keywords— Parkinson’s disease, machine learning, feature
selection, voice data, genetic algorithm

I. INTRODUCTION
Past few years a lot of research has been going on the
Parkinson’s disease because the healthcare related cost due tothis disease is keeping on increasing as the longevity of the
population is increasing in the developed countries. Since thisdisease affect most of the old people, it is become necessary
for the developed counties to detect the disease at the earlystage. The early detection will help the developed country ineconomic perspective as well as social perspective because it
can be assessed well. Parkinson’s disease belongs to one of
the category of neurodegenerative disease which directly aswell as indirectly affects the brain cells that will affect the
movement, speech and other cognitive parts [1, 2, and 3]. The
Parkinson’s disease is progressive in nature. As the diseaseprogresses more than 90% of the patients has the speechdisorder [4]. The symptom related to the vocal impairment of
Parkinson’s disease patients is called dysphonia. The
clinicians measured some indicators related to dysphonia toassess the PD patients. The measures related to dysphonia

Copyright ⓒ 2018 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 7, Issue 3, May 2018

could be treated as an important and most reliable tool toassess the voice related problem and monitor it at different
stage [5, 6]. Usually the measures have lot of features which
does not helpful for machine learning approaches, so feature
selection method has been used for proper assessment. The
feature selection method will help to evaluate the important
contribution of the features in the assessment of the disease atdifferent stage and also it helps to achieve good accuracy [7,8].
The traditional diagnosis needs lot of observations related
to the daily living activities, motor skills and otherneurological parameters to assess the progression of PD, but
this process is not suitable for the early detection of the PD.
With respect to the past research it is found that artificial
intelligence and machine learning techniques have goodpotential for the classification and it also found that theclassification system helps to improve the accuracy and thereliability of the diagnosis and also minimize the errors aswell as make the system more efficient [9]. improvement on
the prediction of accuracy on the progression of PD is getting
lot of attention these days [10, 11].
So in this paper an attempt has been made to check the
improvement in the accuracy while classifying the PD groupfrom the healthy control group by using different machinelearning algorithm with different feature sets such as thegenetic algorithm (GA) based feature set as well as the PCA
based feature sets. Finally, a comparison has made in terms of
performance metrics using different feature sets. The structureof the paper is organized as follows: Section 2 presents thepast work related to classification model used for voice
datasets. Section 3 describes about the methodologies used forthis research work. Section 4 describes about the result offeature selection as well as the result of classification. Section
5 describes about the conclusion and future work.
II. RELATED WORK
Some of the Past research works are mentioned below to giveoverall ideas about the amount of work has been done in thisfield. Shahbaba and Neal used nonlinear based approach forclassification of PD. They have used Dirichlet process
mixtures and compared the results with other classification
model such as decision trees, support vector machine andmultinomial logit model and they found Dirichlet processbased method provides best classification approach of 87.7%
compared to the other model [12]. Sakar and Kursun usedfeature selection method as well as machine learning based
method for diagnosis of PD. They have used mutual
information-based feature selection and support vector
machine as the classification approach and they found theirapproach gives an accuracy of 92.75% [13].
Li et al used fuzzy based method to extend theclassification related information and then they have usedprincipal component analysis-based method for feature
selections and the optimal features has been integrated with
SVM based method provides a good accuracy of 93.47% [14].
Spadoto et al used evolutionary base techniques for feature
selection and they have used Optimum-path Forest Classifier

1117

to detect the Parkinson‘s disease and they found this approachprovides a best accuracy of 84.01% while detecting the PD[15].
Luukka have proposed a feature selection method based on
the fuzzy entropy measure and used similarity classifiers toclassify PD. The best classification accuracy obtained by thatmethod was 85.03% [16]. AStröm and Koker proposed amethod that is used parallel feed-forward neural networkbased approach to predict the PD. They have found the modelis robust and the best classification accuracy obtained fromthat approach is 91.20% [17]. Nilashi et al proposed a method
for the prediction of PD progression using clustering andprediction methods. They have applied Adaptive Neuro-Fuzzy
Inference system (ANFIS) and Support Vector Regression forprediction of PD progression. They found this proposed
method helps to improve the accuracy of the progression of
PD [18].
Abdulhay et al proposed a method to investigate gait andtremor by using machine learning techniques based on the gait
data. They have extracted various gait features using the peakdetection and pulse duration and they found accuracy of
92.7% for diagnosis of parkinson‘s disease [19]. Er et al
proposed a method to distinguish dementia patients (AD) fromthe age-related cognitive decline (ARCD) by using machinelearning techniques. They found that these techniques are able
to distinguish ARCD and AD at a success rate of 100% basedon neurocognitive tests [20]. Zeng et al proposed a method toclassify the patients with PD. They have used deterministic
learning method to distinguish PD from healthy control group.
They found accuracy, sensitivity and specificity of 96.39%,
96.77% and 95.89% respectively [21]. Armañanzas et al
proposed a method to find the non-motor related PD severity
based on the machine learning approach. They have used
Hoehn & Yahr index and clinical impression of severity index
as the measure of the severity for the PD patients. They haveused classification with feature selection using evolutionary
algorithm and found accuracy ranging from 72-92% [22].
Kubota et al. discussed about the benefits of using wearable
sensors for measuring the gait parameters and also mentioned
about signal processing and machine learning approaches used
for extracting meaningful information from data. This kind of
information helps to understand the potentials of thetechnology on PD research and practices [23].
Abós et al proposed a method distinguishing the PD
patients based on cognitive status using machine learningtechnique. They have used randomized logistic regression forfeature selection and SVM for machine learning and foundaccuracy of 82.6%. They found connection-wise patterns offunctional connectivity may be helpful for distinguishing the
PD patients. [24]. Singh and Samavedham proposed amethodology using machine learning tools as statisticalmeasure for determining most important features todistinguish different neurological disease(ND). They havefound accuracy of 99.93% while distinguishing various ND
such as HC, PD and SWEDD patients.
The above past works motivated us to try a different
approach. In this paper we have tried different feature

Copyright ⓒ 2018 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 7, Issue 3, May 2018selection approach such as principal component analysis(PCA) approach and genetic algorithm-based approach andthen the performance measures are compared with different
machine learning classifiers.
III. PROPOSED TECHNIQUE
The flow chart of the proposed methodologies is shown in the
Fig 1. In this paper we have used the dataset created by Max
little University Oxford, in collaboration with the National
Centre for Voice and Speech, Denver, Colorado, whorecorded the speech signals [26]. The original data collectedfrom the dataset composed of voice measurements from 31
people out of which 23 were diagnosed with PD. We haveused Principal Component Analysis (PCA) algorithm on theoriginal feature sets.

1118

components or the latent variables are obtained from thevariance of the data by maximizing it. The number ofprincipal components is lesser than the regular variables. PCA
reduces the dimensionality of the space so that the data can bevisualized in the low dimensional space. The feature selection
process is done by removing the redundant variables. [27] We
have found 11 features after implementing the algorithm to theoriginal feature sets. We have also used genetic algorithm(GA)-based feature sets for feature selection. Pledsoe firstpresented an adaptive optimization search methodology iscalled genetic algorithm and Holland mathematicallypresented the genetic algorithm-based approach by getting
inspiration from Darwin‘s theory of evolution. A variable ismentioned as a gene. A chromosome is nothing but a sequenceof gene. An initialization is done randomly by using
population of chromosome. The quality of the chromosomes isevaluated according to a predefined fitness function. Highperformance chromosomes are used to produce the offspring.
The genetic operators such as mutation and crossover are used
to form the offspring.
In this process the chromosomes
are competing with each other and the fittest one survives atthe end. The optimal solution comes after a series of iterative
computations. [28, 29].
We have found 10 features using GA based feature sets. We
have used different classification approach such as RPART,
C4.5, PART, Bagging classification and Regression tree
(Bagging CART), Random Forest, Boosted C5.0 and SVM.
Except SVM all other classifiers are belongs to nonlinear
decision tree-based classifier. Decision tree-based classifiers
are famous for giving good accuracy. SVM is most popularlinear classification method. It uses different kernel function
while doing classification. In this paper we have used radialbasis function kernel function because it is recommended bymany well-known data scientists. After implementing
different classification approach, we have compared theperformance measures such as accuracy, sensitivity,
specificity, PPV and NPV.

Performance Metrics

Fig. 1. Flowchart of the proposed method

The parameters used to compare the performance andvalidations of classifier are as follows: accuracy, sensitivity,
specificity, positive predictive value (ppv), negative predictive
value (npv). The sensitivity is defined as the ratio of true
positives to the sum of true positives and false negatives. The
specificity is defined as the ratio of true negatives to the sumof false positives and true negatives. In our research we haveused the Positive predictive value and negative predictive
value to check the present and absent of disease. So, the ppv isthe probability that the disease is present given a positive test
result and npv is the probability that the disease is absent
given a negative test result [30]. Accuracy is defined as theratio of number of correct predictions made to the total
prediction made and the ratio is multiplied by 100 to make itin terms of percentage.

The most widely applied technique for data reduction andfeature selection is done using PCA. In PCA, the principal

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 7, Issue 3, May 2018

Fig .3 shows that random forest has highest sensitivity of
0.9985 with GA based feature sets followed by SVM with
RBF classifiers with the same feature sets. The SVM
classifier has a sensitivity of 0.9756.
C. Comparison of Specificity

Comparison of Specificity
1
0.8
Specificity

IV. RESULT AND DISCUSSIONS
We have used R programming language to write the code.
We have reduced the original feature sets by feature selection
techniques. We have chosen the PCA and GA as feature
selection technique because these techniques are more widelyused for feature selection without affecting the performance.
As we have already discussed about the PCA method, after
implementing it removes some redundant features and the
reduced features are able to provide 99% variance, without
affecting the performance. The GA method is different, whichneeds lot of iteration to reach the optimal result. With lot of
iteration we have found optimized result with fewer featurescompared to the original features. We have trained eachclassifier based on the trained data and predict the power ofclassifier on the test data. The ratio of train data to test data is
70:30. In case of SVM we have used different Kernel function
such as linear, polynomial, and radial basis function. So, eachclassifier able to show all the performance metrics based on
the test data. We have plot the graphs shown below.

1119

0.6
0.4

GA
PCA

0.2

0

A. Comparison of Accuracy

RPART

C4.5

PART Bagging Random Boosted
CART Forest C5.0

SVM

ClassifiersComparison of Accuracy
1Fig. 4. Specificity of different classifiers
Accuracy

0.8
0.6
0.4

GA
PCA

0.2

Fig. 4 shows that SVM with GA based feature set has
highest specificity of 0.9987. RPART and random forest
also follows SVM with good specificity with the samefeature sets.

0
RPART

C4. 5

PART

Bagging Random Boosted
CART Forest
C5.0

SVM

D. Comparison of PPVClassifiersFig. 2. Accuracy of different classifiersFig. 2 shows that SVM with RBF has highest accuracy of
97.57% with GA based feature sets followed by random forest
and RPART classifiers with the same feature sets. Even
though the other classifiers have less accuracy than SVM, but
the difference in accuracy is not much.
B. Comparison of Sensitivity

PPV

Comparison of PPV
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

GA
PCA

RPART

Comparison of Sensitivity
1

C4.5

PART

Bagging Random Boosted
CART
Forest
C5.0

SVMClassifiers

Sensitivity

0.8
0.6

Fig. 5. PPV of different classifiers

0.4

GA

PCA

0.2
0
RPART

C4.5

PART

Bagging Random Boosted
CART Forest
C5.0

SVM

Fig. 5 shows that the GA based feature set perform better
compared to the PCA based feature set with random forestclassifier. The maximum specificity achieved with thiscombination is 0.9934.

ClassifiersFig. 3. Sensitivity of different classifiers

Copyright ⓒ 2018 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 7, Issue 3, May 2018classifiers provides highest accuracy of 97.57% with GA
based feature sets. This type of analysis will save the timeand efficiency while doing pattern classification
comprising two groups such as PD and control groups.
This will help the clinician to distinguish PD and control
groups. This research can be further improvised by adding
more features and trying other classification techniques.

E. Comparison of NPV

Comparison of NPV
1
0.8

NPV

1120

0.6

REFERENCES

0.4

GA

[1]

PCA

0.2

[2]

0
RPART

C4.5

PART

Bagging Random Boosted
CART
Forest
C5.0

SVMClassifiers

[3]
[4]

Fig. 6. NPV of different classifiersFig. 6 shows that SVM with GA based feature sets
performs better compared to the PCA based feature sets. GA
based feature sets shows maximum npv of 0.9995.

[5]

[6]

From the above plots we have seen that most of the classifiers
are performing better while distinguishing between the
Parkinson‘s patients to the control group. Basically, thefeature selection processes help to save the time and space byremoving the redundant sets, which has almost no impact on
the performance. In our case we have seen the SVM andrandom forest are doing well in terms of the performance
metric comparison with the GA based feature sets. We haveseen that the GA based feature sets are performing wellcompared to PCA based feature sets. In theoretical case we
may give a suggestion based on the accuracy while choosing
the classifier, however in practical case lot of otherperformance measures is also come into picture. In our case
we can suggest SVM is the best classifier and GA is best forfeature selection, however it may provide different result withbigger dataset. This result actually gives an idea about theperformance comparison and also gives an impression to
analyze more deeply for implementing in practical life.

[13]

V. CONCLUSION AND FUTURE WORK

[14]This paper outlined some of the new feature selection
technique as well as some of the supervised machinelearning approach for distinguishing Parkinson‘s patientfrom the control group. We have seen the capability of the
PCA based feature selection technique and GA based
feature selection technique. We have also seen theperformance of different classification approach whencombined with different feature sets. We have found that
GA based feature selection technique performs better
when combined with SVM classifier in terms of all
performance measure compared to the PCA based feature
set. Mostly while looking for performance measure weusually concentrate on accuracy, in that respect our SVM

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Satyabrata Aich is working as a researcher inthe field of computer engineering He has overfour years of teaching, research and industry
experience in India and abroad. He has
published many research papers in journals andconferences in the realms of Supply Chain
Management and data analytics. His researchinterests are natural language processing,
Machine learning, supply chain management,
data mining.
Hee-Cheol Kim received his BSc at
Department of Mathematics, MSc at
Department of Computer Science in SoGang
University in Korea, and PhD at Numerical

1121

Analysis and Computing Science, Stockholm University in Sweden in
2001. His primary concerns are u-Healthcare, smart home technology,e-learning and Human Computer Interaction (HCI). He is associate
professor at Department of Computer Engineering, Inje University in
Korea. He has published many research papers in journals andconferences in the realms of HCI and CSCW.
Young a Kim is a licensed Physical therapist
and has received her Masters degree inCounseling and Psychology in 2008. She isworking as a lecturer for Clinical Kinesiology
and Functional Anatomy at Inje University. She
is also a PhD candidate in the Department of
Rehabilitation Science at the same University.
Her research focuses on assessment tools andrehabilitation for the elderly, especially for thedetection and treatment of age related
conditions. She is a co-author of the book ‗Community Occupational
Therapy‘ and a frequent contributor to the journal of ‗Physical Therapy
Science‘ and a member in the Korean Ageing Friendly Industry
Association as well as the Society of Occupational Therapy for the
Aged and Dementia.
Kueh Lee Hui is working as an assistantprofessor at the department of ElectricalEngineering, Dong-A University since 2012.
She completed her PhD Degrees from
Department of Electrical Engineering, DongA University, Korea. In 2009 she completed
her BS degree in Electronic andCommunication, Department of Electronic
Engineering, University Malaysia of Sarawak,
Malaysia. She also done MS in 2007 from
Malaysia. Her research interests are image processing, face recognition,
digital image forensic, intelligent control and control application,
power system.
Ahmed Abdulhakim Al-Absi is an assistantprofessor in Department of Computer
Engineering(SmartComputing)at
Kyungdong University in South Korea. He
earned a Ph.D. in computer science fromDongseo University in 2015. He received
M.Sc. degree in information technology at
University Utara Malaysia in 2011, and B.Sc.
degree in computer applications at Bangalore
University in 2008. His research interests include Big Data processing,
Hadoop, Cloud computing, IoT, Distributed systems, Parallel
computing, Bioinformatics, Security, and VANETs.
Mangal Sain received the M.Sc. degree incomputer application from India in 2003 and the
Ph.D. degree in computer science in 2011. Since
2012, he has been an Assistant Professor with
the Department of Computer Engineering,Dongseo University, South Korea. His researchinterest includes wireless sensor network, cloudcomputing, Internet of Things, embedded
systems, and middleware. He has authored over
50 international publications including journals
and international conferences. He is a member of TIIS and a TPC
member of more than ten international conferences.

Copyright ⓒ 2018 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 7, Issue 3, May 20181122

Prediction of Quality for Different Type of Winebased on Different Feature Sets Using Supervised
Machine Learning Techniques
Satyabrata Aich*, Ahmed Abdulhakim Al-Absi**, Kueh Lee Hui***, and Mangal Sain*******Department of Computer Engineering, Inje University, South Korea
Department of Computer Engineering, Kyungdong University- Global Campus, Gangwondo, South Korea***
Dept. of Electrical Engineering, Dong-A University, South Korea****
Department of Computer Engineering, Dongseo University, South Korea
satyabrataaich@gmail.com, absiahmed@kduniv.ac.kr, leehkueh@dau.ac.kr, mangalsain1@gmail.com

Abstract—In recent years, most of the industries
promoting their products based on the quality certification
they received on the products. The traditional way ofassessing the product quality is time consuming, howeverwith the invent of machine learning techniques theprocesses has become more efficient and consumed lesstime than before. In this paper we have explored, some of
the machine learning techniques to assess the quality ofwine based on the attributes of wine that depends on
quality. We have used white wine and red wine quality
dataset for this research work. We have used different
feature selection technique such as genetic algorithm (GA)based feature selection and simulated annealing (SA)based feature selection to check the prediction
performance. We have used different performancemeasure such as accuracy, sensitivity, specificity, positive
predictive value, negative predictive value for comparison
using different feature sets and different supervisedmachine learning techniques. We have used nonlinear,linear and probabilistic classifiers. We have found thatfeature selection-based feature sets able to provide better
prediction than considering all the features for
performance prediction.

———————————————————————
Manuscript received January 02, 2018. This work was supported by
Kyungdong University grant, 2018 and a follow-up the invited journal to theoutstanding paper of the 20th International Conference on Advanced
Communication Technology (ICACT 2018).
Satyabrata Aich is with the Department of Computer Engineering, Inje
University, South Korea (e-mail: satyabrataaich@gmail.com)
Ahmed Abdulhakim Al-Absi is with the Department of Computer
Engineering, Kyungdong University- Global Campus, Gangwondo, South
Korea (e-mail: absiahmed@kduniv.ac.kr)
Kueh Lee Hui is with the Department of Electrical Engineering, Dong-A
University, South Korea (e-mail: leehkueh@dau.ac.kr)
Mangal Sain is with the Department of Computer Engineering, Dongseo
University, South Korea. He is the corresponding author of this paper.
(Corresponding
authorphone:+8251-320-2009;e-mail:mangalsain1@gmail.com).

We have found accuracy ranging from 95.23% to98.81% with different feature sets. This analysis will help
the industries to access the quality of the products at lesstime and more efficient way.
Keywords—machine learning; feature selection; classifiers;
performance metrics;wine qulaity

I.

INTRODUCTION

In recent years there is a modest increase in the wine
consumption as it has been found that wine consumption has apositive correlation to the heart rate variability [1]. With theincrease in the consumption wine industries are looking foralternatives to produce good quality wine at less cost. Different
wines have different purposes. Although most of the chemicals
are same for different type of wine based on the chemical tests,
the quantity of each chemicals have different level ofconcentration for different type of wine. These days it is really
important to classify different wine for quality assurance [2]. In
the past due to lack of technological resources it become
difficult for most of the industries to classify the wines basedon the chemical analysis as it takes lot of time and also needmore money. These days with the advent of the machinelearning techniques it is possible to classify the wines as wellas it is possible to figure out the importance of each chemical
analysis parameters in the wine and which one to ignore for
reduction of cost. The performance comparison with different
feature sets will also help to classify it in a more distinctive
way. In this paper an intelligent approach is proposed byconsidering genetic algorithm (GA) based feature selection as
well as simulated annealing-based feature selection considering
the nonlinear classifiers, linear classifiers and probabilisticclassifiers to predict the quality in red wine as well as the whitewine.
The structure of the paper is organized as follows: Section
II presents the past work related to this field. Section 3
describes about the methodologies used for this research work.
Section 4 describes about the result of feature selection as well

Copyright ⓒ 2018 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 7, Issue 3, May 2018as the result of classification. Section 5 describes about theconclusion and future work.
II.

RELATED WORKSIn the past few attempts have been made to use different
machine learning approaches and feature selection techniques
to the wine dataset. Er and Atasoy proposed a method toclassify the quality of wines using three different classifier suchas support vector machines, Random forest and k-nearest
neighborhood. They have used principal component analysis
for feature selection and they found good result using Random
forest algorithm [3]. Chen et al proposed an approach that will
predict the grade of wine using the human savory reviews.
They have used hierarchical clustering approach andassociation rule algorithm to process the reviews and predict
the wine grade and they found an accuracy of 85.25% while
predicting the grade [4]. Appalasamy et al proposed a method
to predict wine quality based on physiochemical test data. They
have pointed out that classification approach helps to improve
the quality of wine during the production [5]. Beltrán et al
proposed an approach to classify the wine based on aroma
chromatograms and they have used PCA for dimensionality
reduction and wavelet transform for feature extraction andclassifiers such as neural network, linear discriminant analysis
and support vector machine and found that support vector
machine with wavelet transforms perform better than otherclassifiers [6]. Thakkar et al., used analytical hierarchy process(ahp) to rank the attributes and then used different machinelearning classifiers such as support vector machine and random
forest and they found accuracy of 70.33% using random forestand 66.54% using SVM [7]. Reddy and Govindarajulu used auser centric clustering approach to recommend the product.
They have used red wine data set for the survey purpose. They
have allocated relative voting to the attributes based on theliterature review. Then they assigned weight to the attributes
using Gaussian Distribution Process. They judged the quality
based on the user preference group [8]. The above past work
motivated us to try different feature selection algorithm as well
as different classifiers to compare the performance metrics.
This paper proposed GA based feature selection and SA based
feature selection and used different classifiers such as PART,
RPART, Bagging, C5.0, random forest, svm, lda, naïve bayes
etc.
III.

1123

a gene. A chromosome is nothing but a sequence of gene. An
initialization is done randomly by using population ofchromosome. The quality of the chromosomes is evaluatedaccording to a predefined fitness function. High performance
chromosomes are used to produce the offspring. The genetic
operators such as mutation and crossover are used to form theoffspring.
In this process the chromosomes arecompeting with each other and the fittest one survives at theend. The optimal solution comes after a series of iterative
computations. [10, 11].

METHODOLOGIESThe flow chart of the proposed methodology is shown in
the Fig. 1.
A. Data Collection
The wine data set is publicly available in the database ofUCI. The two datasets are related to red and white variants of
the Portuguese "Vinho Verde" wine. This data set contains thephysiochemical variables as well as sensory variables;
altogether there are 12 attributes [9]. We have used genetic
algorithm (GA)-based feature sets for feature selection. Pledsoe
first presented an adaptive optimization search methodology iscalled genetic algorithm and Holland mathematically presented
the genetic algorithm-based approach by getting inspirationfrom Darwin’s theory of evolution. A variable is mentioned as

Fig. 1. Flow chart of proposed method

We have also used simulated annealing-based feature set
for feature selection. The widely used combinatorial
optimization method is called simulated annealing. It is one of
the most popular search algorithms. This method usedprobabilistic technique to find the local optima that ultimately
find a better solution [12]. This method is widely used forfeature selection method. The simulated algorithm procedure ismentioned below. It runs based on the number of classes. If thenumber of classes is n then it runs nth times. In each run j, theCopyright ⓒ 2018 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 7, Issue 3, May 2018

B. Performance Measure Metrics
The parameters used to compare the performance andvalidations of classifier are as follows: accuracy, sensitivity,
specificity, positive predictive value (ppv), negative predictive
value (npv). The sensitivity is defined as the ratio of true
positives to the sum of true positives and false negatives. The
specificity is defined as the ratio of true negatives to the sumof false positives and true negatives. In our research we haveused the Positive predictive value and negative predictive
value to check the present and absent of one type of wine. So,
the ppv is the probability that the one type of wine is present
given a positive test result and npv is the probability that theone type of wine is absent given a negative test result [14].
Accuracy is defined as the ratio of number of correct
predictions made to the total prediction made and the ratio is
multiplied by 100 to make it in terms of percentage.

B. Comparison of Sensitivity for red wine
Fig. 3 shows the sensitivity plot of all the classifiers with two
different feature sets. The plot shows SVM has the highest
sensitivity compared to others and it was found to be 0.9717
with the SA based feature sets

Comparison of Sensitivity
1

0.8
0.6
0.4

GA

SA

0.2
0
RPART

C4.5

PART Bagging Random Boosted SVM
CART Forest C5.0

LDA

NB

Classifiers

RESULTS AND DISCUSSION

We have divided the data into two groups such as train data
and test data. We trained each classifier based on the trained
data and predict the power of classifier on the test data. So,each classifier able to show all the performance metrics such as
accuracy, sensitivity, specificity, PPV, and NPV based on thetest data. We have applied all the classification techniques to
the GA based reduced feature sets for two types of wine as wellas SA based reduced feature sets for two types of wine tomeasures the performance parameter with respect to eachclassifier. We separated each performance measures withrespect to GA and SA sets and plot the column plot for better
visualization. The results of each performance measure withrespect to two feature sets are shown in the Fig. 2, 3, 4, 5, and 6
respectively for red wine and 7, 8, 9, 10, 11 for white wine.
A. Comparison of Accuracy for red wine

Fig. 3. Comparison of Sensitivity on PCA and RFE sets

C. Comparison of Specificity for red wine

Comparison of Specificity
1
0.8
Specificity

IV.

Fig. 2 show that SVM classifier shows maximum accuracy
among all the classifiers. It is performed better with the SA
based feature sets. The accuracy of SVM classifier with SA
feature set found to be 95. 23%.

Sensitivity

subset of the feature for the jth class is found. All the jth classpatterns are taken into one class and other pattern belong to theother class while evaluating the current string. This process
helps to give the features which classify patterns as belonging
to class j or not class j [13]. After selecting the features byusing simulated annealing (SA) and genetic algorithm (GA),
we have implemented the data sets into various classifiers andcompare the performance parameters.

1124

0.6
0.4

GA
SA

0.2

0
RPART

C4.5

PART Bagging Random Boosted SVM
CART Forest C5.0

LDA

NB

ClassifiersComparison of Accuracy
1Fig. 4. Comparison of Specificity on PCA and RFE sets

Accuracy

0.8
0.6
0.4

GA
SA

0.2

Fig. 4 shows that SVM classifier shows maximum specificity
among all the classifiers. It is performed better with the SA
based feature sets. The specificity of SVM classifier with SA
feature set found to be 0.9835

0
RPART

C4.5

PART Bagging Random Boosted SVM
CART Forest C5.0

LDA

NB

D. Comparison of PPV for red wine

ClassifiersFig. 2. Comparison of Accuracy on PCA and RFE sets

Copyright ⓒ 2018 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 7, Issue 3, May 20181125

Fig. 7 shows that SVM classifier shows maximum accuracy
among all the classifiers. It is performed better with the SA
based feature sets. The accuracy of SVM classifier with SA
feature set found to be 98. 81% for white wine data set.

Comparison of PPV
1
0.8

PPV

0.6
0.4

GA

G. Comparison of Sensitivity for white wine

SA

0.2

Comparison of Sensitivity

0
RPART

C4.5

PART Bagging Random Boosted SVM
CART Forest C5.0

LDA

NB

1

ClassifiersFig. 5. Comparison of PPV on PCA and RFE sets

Fig. 5 shows the PPV plot of all the classifiers with two
different feature sets. The plot shows SVM has the highest
PPV compared to others and it was found to be 0.9912 with
the SA based feature sets

Sensitivity

0.8
0.6
0.4

GA

SA

0.2
0
RPART

C4.5

PART Bagging Random Boosted SVM
CART Forest C5.0

LDA

NB

Classifiers

E. Comparison of NPV for red wine

Fig. 8. Comparison of Sensitivity on PCA and RFE sets

Comparison of NPV

Fig. 8 shows the sensitivity plot of all the classifiers with two
different feature sets. The plot shows SVM has the highest
sensitivity compared to others and it was found to be 0.9934
with the SA based feature sets for white wine data set.

1

NPV

0.8
0.6
0.4

GA

H. Comparison of Specificity for white wine

SA

0.2

0
RPART

C4.5

PART

Bagging Random Boosted
CART Forest
C5.0

SVM

LDA

Comparison of Specificity

NB

1

ClassifiersFig. 6. Comparison of NPV on PCA and RFE sets

Fig. 6 shows that SVM classifier shows maximum NPV
among all the classifiers. It is performed better with the SA
based feature sets. The NPV of SVM classifier with SA
feature set found to be 0.9907

Specificity

0.8
0.6
0.4

GA
SA

0.2

0
RPART

C4.5

PART Bagging Random Boosted
CART Forest C5.0

SVM

LDA

NB

Classifiers

F. Comparison of Accuracy for white wine
Fig. 9. Comparison of specificity on PCA and RFE sets

Fig. 9 shows that SVM classifier shows maximum specificity
among all the classifiers. It is performed better with the SA
based feature sets. The specificity of SVM classifier with SA
feature set found to be 0.9956 for white wine data set.

Comparison of Accuracy
1

Accuracy

0.8
0.6

I. Comparison of PPV for white wine

0.4

GA
SA

0.2

0
RPART

C4.5

PART Bagging Random Boosted SVM
CART Forest C5.0

LDA

NB

ClassifiersFig. 7. Comparison of Accuracy on PCA and RFE sets

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 7, Issue 3, May 2018

V.

Comparison of PPV

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CONCLUSION AND FUTURE WORK

1
0.8

PPV

0.6
0.4

GA
SA

0.2

0
RPART

C4.5

PART Bagging Random Boosted
CART Forest C5.0

SVM

LDA

NB

ClassifiersFig. 10. Comparison of PPVon PCA and RFE sets

Fig. 10 shows the PPV plot of all the classifiers with two
different feature sets. The plot shows SVM has the highest
PPV compared to others and it was found to be 0.9987 with
the SA based feature sets for white wine data set.

J. Comparison of NPV for white wine

This paper mentioned about potential of genetic algorithm
as well as simulated annealing algorithm for feature selection
as well as the potentials of the classifiers to predict accurately
based on the new feature sets. The feature selection algorithm
provided a clear idea about the importance of the attributes forprediction of quality, which was time consuming andexpensive when done in the traditional way. We have also
compared the performance metrics of linear, nonlinear, andprobabilistic based classifiers and it was found that theseclassifiers performed well with the new feature sets. We havefound that the SA based feature sets performed better than the
GA based feature sets. We have also found that the SVM
classifier performed better compared to all other classifiers for
red wine and white wine data sets. We have found accuracy
ranging from 95.23% to 98.81% with different feature sets. In
future we can try other performance measures and othermachine learning techniques for better comparison on results.
This analysis will help the industries to predict the quality ofthe different type of wines based on certain attributes and also
it will helpful for them to make good product in the future.

Comparison of NPVREFERENCES

1

NPV

0.8

[1]

0.6

0.4

GA
SA

0.2
0

RPART

C4.5

PART

Bagging Random Boosted
CART Forest
C5.0

SVM

LDA

NB

ClassifiersFig. 11. Comparison of NPV onPCA and RFE sets

Fig. 11 shows that SVM classifier shows maximum NPV
among all the classifiers. It is performed better with the SA
based feature sets. The NPV of SVM classifier with SA
feature set found to be 0.9992 for white wine data set.
The above plots show the performance metrics comparison ofdifferent type of wines based on the metrics parameters suchas accuracy, sensitivity, specificity, ppv and npv on two
different feature sets. The result shows that the SVM classifier
performs better for both type of data sets. Specially it isperforming better in SA based feature sets. Although it is easy
to say based on our result that Simulated Annealing is better
algorithm for feature selection compared to genetic algorithmbased feature selection method, however the result could be
different for other datasets as well as it could be different forbigger datasets. Similarly based on our result we can say that
SVM classifier is best, but in practical lot of other parameters
also come into picture that could change the scenario
completely. This analysis will give a clear idea about theimportant attributes for the prediction of quality as well as itsaves lot of time and money for the industries.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 7, Issue 3, May 2018
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Satyabrata Aich is working as a researcher inthe field of computer engineering He has overfour years of teaching, research and industry
experience in India and abroad. He has
published many research papers in journals andconferences in the realms of Supply Chain
Management and data analytics. His researchinterests are natural language processing,
Machine learning, supply chain management,
data mining.

Kueh Lee Hui is working as an assistantprofessor at the department of ElectricalEngineering, Dong-A University since 2012.
She completed her PhD Degrees from
Department of Electrical Engineering, Dong-A
University, Korea. In 2009 she completed her
BS degree in Electronic and Communication,
Department of Electronic Engineering,
University Malaysia of Sarawak, Malaysia. She
also done MS in 2007 from Malaysia. Her

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research interests are image processing, face recognition, digital image
forensic, intelligent control and control application, power system.
Ahmed Abdulhakim Al-Absi is an assistantprofessor in Department of Computer
Engineering (Smart Computing) at Kyungdong
University in South Korea. He earned a Ph.D.
in computer science from Dongseo University
in 2015. He received M.Sc. degree in
information technology at University Utara
Malaysia in 2011, and B.Sc. degree incomputer applications at Bangalore University
in 2008. His research interests include Big
Data processing, Hadoop, Cloud computing, IoT, Distributed systems,
Parallel computing, Bioinformatics, Security, and VANETs.
Mangal Sain received the M.Sc. degree incomputer application from India in 2003 and the
Ph.D. degree in computer science in 2011. Since
2012, he has been an Assistant Professor with
the Department of Computer Engineering,Dongseo University, South Korea. His researchinterest includes wireless sensor network, cloudcomputing, Internet of Things, embedded
systems, and middleware. He has authored over
50 international publications including journals
and international conferences. He is a member of TIIS and a TPC
member of more than ten international conferences.

Copyright ⓒ 2018 GiRI (Global IT Research Institute)