Engineering, Technology & Applied Science Research Vol. 8, No. 2, 2018, 2790-2795 2790 
 

www.etasr.com Alghobiri: A Comparative Analysis of Classification Algorithms on Diverse Datasets 
 

A Comparative Analysis of Classification Algorithms 
on Diverse Datasets 

 

Muhammad Alghobiri 
Management Information Systems Department 

King Khalid University, Abha 
Saudi Arabia 

 

 

Abstract—Data mining involves the computational process to find 
patterns from large data sets. Classification, one of the main 
domains of data mining, involves known structure generalizing to 
apply to a new dataset and predict its class. There are various 
classification algorithms being used to classify various data sets. 
They are based on different methods such as probability, decision 
tree, neural network, nearest neighbor, boolean and fuzzy logic, 
kernel-based etc. In this paper, we apply three diverse 
classification algorithms on ten datasets. The datasets have been 
selected based on their size and/or number and nature of 
attributes. Results have been discussed using some performance 
evaluation measures like precision, accuracy, F-measure, Kappa 
statistics, mean absolute error, relative absolute error, ROC Area 
etc. Comparative analysis has been carried out using the 
performance evaluation measures of accuracy, precision, and F-
measure. We specify features and limitations of the classification 
algorithms for the diverse nature datasets. 

Keywords-data mining; classification algorithms; diverse; 
dataset 

I. INTRODUCTION  
Due to the evolving of computer science and the fast 

development and vast usage of World Wide Web and other 
electronic data, information extraction is a popular research 
field. Data mining [1, 2] is a significant method to extract 
information from data. There are various domains of data 
mining like classification, clustering, anomaly detection, 
association rule mining, regression, pattern mining, 
summarization etc. Data mining has even further involved in 
many other studies like text mining, social network mining, 
influence mining, sentiment mining etc. Data mining utilizes 
the information from existing data to examine the result of a 
specific problem. It analyzes the data that may have been 
extracted or gathered from any business. Decision makers opt 
for data mining to take a decision regarding marketing 
strategies for their products. Data mining interferes data into 
real-life investigation and can be applied to enhance sales, new 
product promotion, or product deletion. Classification [3, 4] is 
one of the main domains of data mining and has extensively 
been used for various purposes like decision making, weather 
forecasting, prediction of customers’ attitude, prediction of 
various social risk analysis as well as official tasks, prediction 
of influential bloggers [5-10] etc. A classification process can 
easily be separated into two main steps. In the first step, a part 

of data known as training data is used and each row or value of 
training dataset comprises a set of characteristics. 
Determination of classes is the main target behind 
classification. The first phase generates the classification model 
known as classifiers that depict the relationship between 
characteristics and classes. In the second phase, the 
classification accuracy of a classification algorithm that has 
been generated by the first stage is analyzed. There are various 
classification algorithms, classified into different categories. 
The category of classification algorithms includes probability 
based Bayesian algorithms, decision tree based algorithms, 
neural network based algorithms and kernel-based algorithms. 
Most classifiers use probability calculations to make class 
labels, however, accuracy measure has not been a target. Naive 
Bayes and the C4.5 learning algorithm are alike in predictive 
accuracy [11-13].  

II. RELATED WORK 
Classification is a very vast domain in data mining, having 

received a great deal of exploring over the last few decades. A 
comparison of four classification algorithms (logistic 
regression, Naïve Bayes, C4.5 decision tree and nearest 
neighbor) has been carried out in standard and boosted forms to 
predict class members for an online community in [14]. The 
comparison has been evaluated using two performance 
measures, one is the area under the curve and another is the 
accuracy in the standard and boosted forms. Analysis 
conducted depicts very significant differences among base 
classification algorithms. Several classification techniques have 
been empirically compared within the analysis of unbalanced 
credit scoring data sets. Traditional classification techniques 
such as neural networks, logistic regression and decision trees 
have been used in order to find the suitability of support vector 
machines, gradient boosting and random forests for loan 
default prediction [15]. The use of data mining is also very 
common in bio-informatics. Authors in [27] emphasized on the 
importance of rule-based decision trees as a classification 
method. There are two types of nondeterministic rules in 
decision tables known as inhibitory rules and bounded rules. In 
the former, we have decisions on one right side while in the 
later we can have fewer decisions on the right side. Two 
classification algorithms of polynomial time complexity 
established on deterministic and inhibitory decision rules have 
been compared. Experiments are executed on five practical 



Engineering, Technology & Applied Science Research Vol. 8, No. 2, 2018, 2790-2795 2791 
 

www.etasr.com Alghobiri: A Comparative Analysis of Classification Algorithms on Diverse Datasets 
 

data sets from the predictive toxicology domain and 
mutagenesis. Later experiments performed on a dataset of 
artificial datasets constructed to evaluate the performance of 
the classifiers on the basis of different parameters [16]. In [17], 
different logical analysis methods were compared for 
hypothetical target classification. This study reveals how pre-
processing can protect result confidence and compares the 
results between multi-quantization, fuzzy and Boolean 
techniques. Classification algorithms are used for a variety of 
purposes including spam filtering [18-20], web page ranking 
calculation for web spam [21], software defect detection [22], 
text classification [23], music emotion classification [24], 
feature-based mining of digital images [25], or annual crop 
classification [26].  

The discussion above reveals that all the existing 
comparative works on classification algorithms have been done 
on algorithms of the same category. This study is a novel in the 
sense that algorithms have been selected with a diverse nature 
and diverse data sets better than those established on 
deterministic decision rules. 

III. RESEARCH METHODOLOGY 

A. Selected Classification Algorithms 
There are numerous classification algorithms, but we have 

focused on algorithms of diverse nature, therefore, three 
different algorithms have been chosen. C4.5 is the famous 
algorithm that is based on the decision tree algorithm, whereas 
the Naïve Bayes is a probabilistic algorithm and the Support 
Vector Machine algorithm (SVM) is a kernel based algorithm. 
The diversity of the proposed algorithms can lead decision-
making to confusion, therefore, in the following, a brief 
description will be introduced. 

Positioning Figures and Tables 

Decision tree is also known as a statistical classifier used in 
classification. The decision tree is produced by C4.5. After a 
tree is built, the C4.5 rule induction program is used to produce 
a set of rules. Trees are shown by C4T and rules by C4R. At 
each node of the tree, one attribute is efficiently split its 
example set into subsets. Information gain is used for splitting. 
Attribute with top value of normalized information gain is 
selected to make the decision. The C4.5 algorithm is recursive 
on the smaller sub-lists. Decision tree divides the features of 
the documents into partitions. Splitting of data reduces the 
chances of errors at every stage. The root node is used to 
examine the branches of the tree to predict a label for new data 
[28]. Data is trained in less time because of the graphical 
representation. We can examine it quickly from root to child 
nodes in which root node depicts the input. To calculate 
decision tree we need to calculate two type of entropies. 

 Frequency table of one attribute. (D) = ∑ −(Pᵢlog₂Pᵢ)   (1) 
 Frequency table of two attributes. E(T,X) = ∑ P(e)е∊ E(e)   (2) Gain(T,X) = Entropy(T) − Entropy(T,X) (3) 

Naïve Bayes (NB) 

It is a probabilistic algorithm, supposing that some features 
are not relevant to other features. Naïve Bayes is used for 
supervised learning methods, parameter estimation for Naive 
Bayes models etc. In a practical environment, this classifier 
performed better than others. Different attempts are taken to 
improve Naïve Bayes for classification [29]. New text is 
presented with t* in a document A. it calculates: t ∗= argmax P(A|As)   (4) 

The Naïve Bayes (NB) classifier uses Bayes’ rule: P(t|A) = ( ) ( | )( )     (5) 
P(A) does not select t*. By supposing conditional 

independence of features	ƒᵢ’ class d, P( | ) is being estimated. 
Training procedure calculates the relative frequency of P(t) and 
P(ƒᵢ|t). P(t|A) = Pɴʙ(t)(	∏ᵐᵢ₌₁P(ƒᵢ|t	ⁿᵢ⁽ᵈ⁾)  (6) 
Support Vector Machine (SVM) 

A learning algorithm which is based on the kernel is used in 
SVM. It is used to identify pattern regression analysis and 
classification and it performs well in text classification. It 
predicts classes by using training data. SVM carries out non-
linear classification efficiently [30]. Training points are 
separated into two categories based on support vectors by using 
decision surface. SVM optimization is calculated as 

1 1 1

arg min{ ( ) }
n n n

i i i i j j j
i i j

y x x y   
  

   


  (7) 

∑ βᵢyᵢⁿ 	 = 0;0 ≤ β ≤ C   (8) 
B. Selected Datasets 

Ten datasets have been considered. Details of each dataset 
is shown in alphabetical order in Table I. 

Contact lenses 

The examples in the dataset are complete. The attributes are 
unable to define all the factors affecting the decision.  

CPU 

It is used for prediction in numeric form on the basis of 
instance-based learning with encoding length selection.  

Credit 

This is a credit related data set which is the largest and 
consists of 15 attributes and results in whether the credit is 
positive or negative. 

Iris-discretized 

A small dataset about iris classification. It is unique in this 
regard as its values are ranging and given in special characters 
and unique style. 

Labor 

This dataset is the most unlike one as the attributes in the 
dataset are of a unique style. Few are having special characters, 



Engineering, Technology & Applied Science Research Vol. 8, No. 2, 2018, 2790-2795 2792 
 

www.etasr.com Alghobiri: A Comparative Analysis of Classification Algorithms on Diverse Datasets 
 

few have enumerated values. There are Boolean attributes as 
well. The attributes are mainly related to wages, pension, 
allowances, assistance, plan, duration period etc. 

Spambase 

This dataset is concerning spam and is a very large set of 
attributes in real format. Usually based on the frequency of 
words or characters or case-based sentences in various 
categories, the attributes can be described as derived values. It 
is unique as its values are in real format and derived attributes 
based values. 

Titanic 

This dataset is related to the famous Titanic sinking event 
and it predicts a person survival level based on its class, age, 
and gender. 

VO 

This dataset is regarding Congress voting. It is very 
interesting in this regard that it has a number of attributes 
regarding factors influencing voters to vote either for 
democrats or republicans.  

TABLE I.  DATASETS  

 Name Attributes 
Instan

ces 
Classes Remarks 

1 
Contact 

lens 
4 24 2 

Values are in 
nominal form 

2 CPU 8 209 1 
Class is in real 

values 

3 Credit 16 490 2 
Predicts whether 

positive or negative 

4 
Iris-

discretized 
5 150 3 

Attributes in 
Special characters 

5 Labor 17 56 2 
Attributed in 

special format, real 
as well as anime 

6 Spambase 58 4600 2 
Predicts whether 

spam or not 

7 Titanic 3 2202 2 
Calculates whether 
individual survived 

or not 

8 VO 17 435 2 
Political field data 
from US elections 

C. Software Used 
Weka workbench provides the facility of visualizing 

attributes and algorithms for predictive analysis. It was built in 
C language, now we found Java-based versions only. It 
supports many tasks related to data mining like clustering, data 

preprocessing, classification, feature selection, visualization 
and regression. The ARFF file consists of two parts: the header 
and the data section. As the minimum number of attributes in 
the datasets is 6, thus, this value has been considered as the 
number of folds for all the algorithms. The following measures 
are used to find the results of the given classifiers: 

Precision 

Precision is denoted as the ratio of retrieved documents that 
are relevant to the search. Precision =	     (9) 
F-Measure 

The f-measure includes both precision and accuracy. It may 
be considered as the weighted average of both values. F = ∗	 ∗		     (10) 
Recall 

Recall is also known as the fraction of relevant instances 
that have been retrieved over the total amount of retrieved 
instances. Recall =	     (11) 
MCC 

MCC is used for measuring the quality of binary 
classification. It takes into account TP and FP and is generally 
known as a balanced measure. |MCC| =	 x /n    (12) 
Kappa Statistic 

Kappa is the most robust method of measuring inter-rater- 
agreement for a qualitative item. K takes into account the 
possibility of the agreement occurring by chance K =   K = -- …(14)  
 (13) 

IV. RESULTS 
All datasets have been taken in ARFF file format, which is 

used in Weka [31, 32] for data mining. The four main diverse 
nature algorithms have been compared twice. Exclusively, the 
datasets have been analyzed extensively as shown in Tables II-
VII below. 

TABLE II.  COMPARATIVE RESULT ANALYSIS FOR NAÏVE BAYES ALGORITHM 

Dataset Accuracy (%) Kappa 
Mean Absolute 

Error (%) 
Relative Absolute 

Error (%) 
Coverage of 
Cases (%) 

Mean -relevant 
Region Size (%) 

Contact lens 70.83 0.43 0.25 67.0 100.0 84.72 
Credit 77.55 0.53 0.23 44.94 87.96 59.49 

Iris-discretized 94.0 0.91 0.03 7.15 98.66 35.33 
Labor 92.98 0.84 0.12 26.25 98.24 65.78 

Spambase 79.28 0.59 0.20 43.45 79.78 50.43 
TicDate 84.5 0.16 0.36 144.2 91.41 58.63 
Titanic 77.87 0.44 0.32 87.83 100.0 99.9 

Voting 90.11 0.79 0.09 20.95 93.1 53.2 



Engineering, Technology & Applied Science Research Vol. 8, No. 2, 2018, 2790-2795 2793 
 

www.etasr.com Alghobiri: A Comparative Analysis of Classification Algorithms on Diverse Datasets 
 

TABLE III.  WEIGHTED AVERAGE OF DETAILED ACCURACY RESULTS FOR NAÏVE BAYES ALGORITHM 

Dataset Accuracy (%) Kappa 
Mean Absolute 

Error (%) 
Relative Absolute 

Error (%) 
Coverage of Cases (%) 

Mean -relevant 
Region Size (%) 

Contact lens 70.83 0.43 0.25 67.0 100.0 84.72 

Credit 77.55 0.53 0.23 44.94 87.96 59.49 

Iris-discretized 94.0 0.91 0.03 7.15 98.66 35.33 

Labor 92.98 0.84 0.12 26.25 98.24 65.78 

Spambase 79.28 0.59 0.20 43.45 79.78 50.43 

TicDate 84.5 0.16 0.36 144.2 91.41 58.63 

Titanic 77.87 0.44 0.32 87.83 100.0 99.9 

Voting 90.11 0.79 0.09 20.95 93.1 53.2 
 

TABLE IV.  COMPARATIVE RESULT ANALYSIS FOR C4.5 ALGORITHM 

Dataset Accuracy (%) Kappa 
Mean Absolute 

Error (%) 
Relative Absolute 

Error (%) 
Coverage of Cases (%) 

Mean relevant 
Region Size (%) 

Contact lens 83.3 0.71 0.15 0.32 91.0 45.0 
Credit 85.3 0.70 0.18 37.8 96.0 86.0 

Iris-discretized 94.0 0.91 0.05 13.4 98.0 44.2 
Labor 57.9 -0.04 0.46 100.0 92.0 93.0 

Spambase 92.9 0.85 0.08 18.6 94.9 55.9 
TicDate 93 -0.01 0.11 100.0 99.9 99.9 
Titanic 78.9 0.42 0.31 71.3 99.7 96.3 
Voting 96 0.92 0.06 12.0 98.6 54.8 

 

TABLE V.  WEIGHTED AVERAGE OF DETAILED ACCURACY RESULTS FOR C4.5 ALGORITHM 

Dataset True Positive Rate False Positive Rate Precision Recall F-Measure MCC ROC Area RPC Area 

Contact lens 0.83 0.09 0.85 0.83 0.83 0.70 0.84 0.81 
Credit 0.85 0.15 0.85 0.85 0.85 0.70 0.87 0.83 

Iris-discretize 0.94 0.03 0.94 0.94 0.94 0.91 0.96 0.90 
Labor 0.57 0.62 0.53 0.57 0.54 -0.05 0.49 0.55 

Spambase 0.93 0.078 0.93 0.93 0.93 0.85 0.93 0.91 
TicData 0.94 0.94 0.88 0.94 0.91 -0.06 0.49 0.88 
Titanic 0.78 0.42 0.82 0.78 0.75 0.50 0.74 0.77 
Voting 0.96 0.04 0.96 0.96 0.96 0.92 0.97 0.95 

 

TABLE VI.  COMPARATIVE RESULT ANALYSIS FOR SVM ALGORITHM 

Dataset Accuracy (%) Kappa 
Mean Absolute 

Error (%) 
Relative Absolute 

Error (%) 
Coverage of Cases 

(%) 
Mean relevant 

Region Size (%) 
Contact lens 70.8 0.43 0.31 88.32 87.5 66.6 

Credit 86.3 0.72 0.13 27.6 86.3 50.0 
Iris-discretized 94 0.91 0.23 53 100 66.6 

Labor 85.9 0.68 0.14 30.6 85.9 50.0 
Spambase 90.6 0.79 0.09 20.2 90.4 50.0 
TicDate 98.9 0.10 0.98 0.98 98 50.0 
Titanic 77 0.43 0.22 51 77.6 50.0 
Voting 96.0 0.91 0.039 8.2 96 50.0 

 

TABLE VII.  WEIGHTED AVERAGE OF DETAILED ACCURACY RESULTS FOR SVM ALGORITHM 

Dataset TP Rate FP Rate Precision Recall F-Measure MCC ROC Area RPC Area 
Contact lens 0.80 0.05 0.80 0.80 0.80 0.74 0.92 0.71 

Credit 0.86 0.12 0.87 0.86 0.86 0.73 0.86 0.82 
Iris-discretized 0.94 0.03 0.94 0.94 0.94 0.91 0.96 0.90 

Labor 0.86 0.19 0.85 0.86 0.85 0.68 0.83 0.80 
Spambase 0.90 0.12 0.90 0.90 0.90 0.79 0.89 0.86 
TicData 0.98 0.11 0.98 0.98 0.98 0.92 0.89 0.92 
Titanic 0.77 0.37 0.77 0.77 0.76 0.45 0.70 0.69 
Voting 0.96 0.04 0.96 0.96 0.96 0.91 0.96 0.94 



 

evi
set
the
use
and
out
on 
res

wit
ma
log
hav
hav
me
pre
car
and

[1] 

[2] 

Engineerin

www.etasr

Thorough a
idence of the t
ts. For compa
e algorithms o
ed classificatio
d F-measure. 
tperforms the

accuracy an
spectively. 

Fig. 1. 

Fig. 2.  

Classification
th the predicti
any classificat
gic and metho
ve applied thr
ve worked ex
easures like 
ecision, recall
rried out the c
d most widely

N. M. Ramos, 
Appliation of 
Hygrothermal 

B. Bakhshinat
data mining a
Education and
2018 

ng, Technology

r.com 

V. COMPA
analysis of th
three algorithm
arative explici
only with resp
on evaluation 
The comparat

e other classif
nd F-measure 

 Comparative

Comparative 

VI. C
n is a main d
ion of classes 
tion algorithm
ods. We have 
ree widely us
xtensively and

accuracy, k
l, F-measure, 
comparative an
y used perform

REFE
J. M. Delgado, R

f Data Mining T
Conditions, Sprin

tegh, O. R. Zaian
applications and 
d Information Tec

y & Applied Sci

ARATIVE ANALY

he above Ta
ms’ effectiven
it evaluation, 
pect to the thr

measures of a
tive analysis c
fication algori

are given in

 

e analysis based o

 

analysis based on

CONCLUSION 

domain of data
based on exis

ms, each estab
gathered diff

sed classificati
d found out v
kappa statisti

ROC area, e
nalysis using t

mance evaluatio

ERENCES 
R. M. Almeida, M
Techniques in th
nger, 2015 

ne, S. ElAtia, D. 
tasks: A survey

chnologies, Vol. 

ience Research

Algho

YSIS 

ables reveals
ness to differen

we have com
ree most comm
accuracy, prec

concludes that 
thms. Compa

n Figures 1 a

on accuracy 

n F-measure 

a mining that 
sting data. The
blished on dif
ferent data set
ion algorithm

various perform
c, mean sta

etc. In additio
three state of t
on measures. 

M. L. Simoes, S. M
he Analysis of 

Ipperciel, “Educ
y of the last 10 

23, No. 1, pp. 53

h V

obiri: A Compa
 

clear 
nt data 

mpared 
monly 
cision, 
SVM 

arisons 
and 2 

 

 

deals 
ere are 
fferent 
ts and 
s. We 
mance 
atistic, 

on, we 
the art 

Manuel, 
Indoor 

cational 
years”, 

37-553, 

[3]

[4]

[5]

[6]

[7]

[8]

[9]

[10]

[11]

[12]

[13]

[14]

[15]

[16]

[17]

[18]

[19]

[20]

[21]

Vol. 8, No. 2, 20

arative Analysis

F. Ahmed, M. 
integration in b
for imbalanced 
Washington, DC

P. G. Clark, C.
Algorithm with
Systems and Te

H. U. Khan, A. 
S. Alowibdi, 
blogosphere: a 
82, 2017 

H. U. Khan, A
influential blog
2015 

U. Ishfaq, H. U
using sentimen
Electronic and 
227-233, April 

U. Ishfaq, H. U
modular appro
Engineering, Vo

H. U. Khan, “M
lexical and non
pp. 161-176, 20

] H. U. Khan, 
subjectivity an
International A
2017 

] A. Patel, S. Ga
Using Data Min
Technology, Vo

] T. Pranckeviciu
Random Forest
Regression Cla
of Modern Com

] P. V. Ngoc, C
algorithm for e
pp. 1-27, Spring

] C. Sibona, J. 
Algorithms on 
AIS Electronic 

] A. Beque, K. C
credit scorecard
Systems, Vol. 1

] N. S. Ketkar, L
classification 
Intelligence and
April 2, 2009 

] R. Dixit, H. 
algorithms usin
Systems, Vol. 2

] T. R. Patil, V. 
URL Analysis 
International J
Computing Sc
Conference on
Intelligence”, pp

] M. Esmaeili, A
System using N
International Jo
5, 2017 

] D. D. Arifin, M
phone Short M
Naive Bayes Cl
Mobile, Bandun

] X. Zhuang, Y. Z
propagation mo
Retrieval Journ

018, 2790-2795

s of Classificati

Samorani, C. B
big data: Feature 
d learning”, IEEE
C, USA, pp. 532-

. Gao, J. W. Grz
h Multiple Scann
echnologies, Vol. 

Daud, U. Ishfaq,
“Modelling to 

survey”, Comput

A. Daud, T. A. M
ggers in a commu

U. Khan, K. Iqb
nt features”, Int
Electrical Engine
11-12, 2016 

U. Khan, K. Iqbal
oach based on 
ol. 16, pp. 505-52

Mixed-sentiment 
n-lexical features”
017 

A. Daud, “Us
nalysis based o

Arab Journal of In

andhi, S. Shetty, 
ning”, Internation
ol. 4, No. 1, pp. 1

us, V. Marcinke
t, Decision Tree, 
assifiers for Text 
mputing, Vol. 5, N

C. V. T. Ngoc, T
english emotiona
ger Berlin Heidelb

Brickey, “A St
a Single Data Se
Library, 2012 

Coussement, R. 
d calibration: An
134, pp. 213-227, 

L. B. Holder, D. J
algorithms”, IE

d Data Mining, N

Singh, “Compa
ng boolean and 
2012, Article No. 

Thakare, S. She
Based Adaptive

Journal of Ele
cience & Engin
n “Advances I
p. 88-90, 2014 

A. Arjomandzadeh
Naive Bayes Me

ournal of Comput

M. A. Bijaksana, 
essage Service (S
lassifier”, IEEE A
ng, Indonesia, pp.

Zhu, C.-C. Chang
odel for web sp
al, Vol. 20, No. 6

5 

ion Algorithms

Bellinger, O. R. Z
generation in mu

E International Co
-539, December 5

zymala-Busse, “M
ning Discretizatio
72, pp. 218-227, 

, T. Amjad, N. Al
identify influen

ters in Human Be

Malik, “MIIB: A 
unity”, PloS One,

bal, “Modeling to
ternational Confe
eering (ICE Cube

l, “Identifying the
sentiment analy

23, 2017 

classification of 
”, Journal of Web

ing machine le
on lexical and 
nformation Techn

B. Tekwani, “H
nal Research Jour
705-1707, 2017 

evicius, “Compar
Support Vector M
Reviews Classifi

No. 2, pp. 221-232

T. V. T. Ngoc, 
al classification”, 
berg, 2017 

tatistical Compar
et”, AMCIS 2012

Gayler, S. Lessm
n empirical analy

2017 

J. Cook, “Empiric
EEE Symposium
Nashville, USA, p

arison of detect
fuzzy techniques
406204, 2012 

erekar, “A Comb
e Technique for 
ectronics, Comm
neering, Special
In Computing, 

h, R. Shams, M. Z
ethod and Featur
ter Applications, 

 “Enhancing spa
SMS) performanc
Asia Pacific Conf
. 80-84, Septembe

g, Q. Peng, F. Khu
pam demotion a
6, pp. 547-574, 20

2794 

s on Diverse Da

Zaiane, “Advant
ulti-relational dat
onference on Big
5-8, 2016 

MLEM2 Rule Ind
on”, Smart Innov
Springer, 2017 

ljohani, R. A. Abb
ntial bloggers i
havior, Vol. 68, p

Metric to identi
, Vol. 10, p. e013

o find the top bl
erence on Comp
e), Quetta, Pakista

e influential blogg
sis”, Journal of

web forum posts
b Engineering, V

arning technique
non-lexical fea

nology, Vol. 14,

eart Disease Pred
rnal of Engineerin

rison of Naïve B
Machines, and L

fication”, Baltic J
2, 2017 

D. N. Duy, “A 
in: Evolving Sy

rison of Classifi
2 Proceedings, pp

mann, “Approach
sis”, Knowledge-

cal comparison of
m on Computa
pp. 259-266, Mar

tion and classifi
s”, Advances in 

bined Naïve Baye
Email Classifica

munication and 
l Issue: Interna

Communication

Zahedi, “An Anti
re Selection Met
Vol. 165, No. 4, 

am detection on m
ce using FP-grow
ference on Wirele
er 13-15, 2016 

urshid, “A unified
algorithm”, Inform
017 

atasets 

age of 
tabases 
g Data, 

duction 
vation, 

basi, J. 
in the 
pp. 64-

ify top 
38359, 

oggers 
puting, 
an, pp. 

gers: a 
f Web 

s using 
Vol. 16, 

es for 
atures”, 
No. 4, 

diction 
ng and 

Bayes, 
ogistic 

Journal 

C4. 5 
ystems, 

fication 
. 1-13, 

hes for 
-Based 

f graph 
ational 
rch 30-

fication 
Fuzzy 

es and 
ation”, 

Soft 
ational 
n and 

i-Spam 
thods”, 
pp. 1-

mobile 
wth and 
ess and 

d score 
mation 



Engineering, Technology & Applied Science Research Vol. 8, No. 2, 2018, 2790-2795 2795 
 

www.etasr.com Alghobiri: A Comparative Analysis of Classification Algorithms on Diverse Datasets 
 

[22] O. F. Arar, K. Ayan, “A Feature Dependent Naive Bayes Approach and 
Its Application to the Software Defect Prediction Problem”, Applied 
Soft Computing, Vol. 59, pp. 197-209, 2017 

[23] L. Jiang, C. Li, S. Wang, L. Zhang, “Deep feature weighting for naive 
Bayes and its application to text classification”, Engineering 
Applications of Artificial Intelligence, Vol. 52, pp. 26-39, 2016 

[24] Y. An, S. Sun, S. Wang, “Naive Bayes classifiers for music emotion 
classification based on lyrics”, IEEE/ACIS 16th International 
Conference on Computer and Information Science (ICIS), Wuhan, 
China, pp. 635-638, May 24-26, 2017 

[25] H. Lad, M. A. Mehta, “Feature Based Object Mining and Tagging 
Algorithm for Digital Images”, in: Proceedings of International 
Conference on Communication and Networks, Singapore, Advances in 
Intelligent Systems and Computing, Vol. 508, pp. 345-352, 2017 

[26] H. Zhang, Q. Li, J. Liu, J. Shang, X. Du, L. Zhao, N. Wang, T. Dong, 
“Crop classification and acreage estimation in North Korea using 
phenology features”, GIScience & Remote Sensing, Vol. 54, No. 3, pp. 
381-406, 2017 

[27] P. Delimata, B. Marszał-Paszek, M. Moshkov, P. Paszek, A. Skowron, 
Z. Suraj, “Comparison of some classification algorithms based on 
deterministic and nondeterministic decision rules”, in: Transactions on 
Rough Sets XII, Springer, pp. 90-105, 2010 

[28] D. Oreski, S. Oreski, B. Klicek, “Effects of dataset characteristics on the 
performance of feature selection techniques”, Applied Soft Computing, 
Vol. 52, pp. 109-119, 2017 

[29] L. Jiang, D. Wang, Z. Cai, X. Yan, “Survey of improving naive bayes 
for classification”, Lecture Notes in Computer Science, Vol. 4632, 
Springer, Berlin, Heidelberg, pp. 134-145, 2007 

[30] C. Cortes, V. Vapnik, “Support-vector networks”, Machine learning, 
Vol. 20, pp. 273-297, 1995 

[31] M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, I. H. 
Witten, “The WEKA data mining software: an update”, ACM SIGKDD 
Explorations, Vol. 11, No. 1, pp. 10-18, 2009 

[32] R. R. Bouckaert, E. Frank, M. A. Hall, G. Holmes, B. Pfahringer, P. 
Reutemann, I. H. Witten, “WEKA-Experiences with a Java Open-Source 
Project”, Journal of Machine Learning Research, Vol. 11, pp. 2533-
2541, 2010