The Impact of Feature Selection on Web Spam Detection

Abstract

Search engine is one of the most important tools for managing the massive amount of distributed web content. Web spamming tries to deceive search engines to rank some pages higher than they deserve. Many methods have been proposed to combat web spamming and to detect spam pages. One basic one is using classification, i.e., learning a classification model for classifying web pages to spam or non-spam. This work tries to select the best feature set for classification of web spam using imperialist competitive algorithm and genetic algorithm. Imperialist competitive algorithm is a novel optimization algorithm that is inspired by socio-political process of imperialism in the real world. Experiments are carried out on WEBSPAM-UK2007 data set, which show feature selection improves classification accuracy, and imperialist competitive algorithm outperforms GA.

Full text

I.J. Intelligent Systems and Applications, 2012, 9, 61-67
Published Online August 2012 in MECS (http://www.mecs-press.org/)
DOI: 10.5815/ijisa.2012.09.08
Copyright © 2012 MECS I.J. Intelligent Systems and Applications, 2012, 9, 61-67
The Impact of Feature Selection on Web Spam
Detection
Jaber Karimpour
Dept. of Computer Science, University of Tabriz, Tabriz, Iran
karimpour@tabrizu.ac.ir
Ali A. Noroozi
Dept. of Computer Science, University of Tabriz, Tabriz, Iran
aliasghar.noroozi@gmail.com
Adeleh Abadi
Dept. of Computer Science, University of Tabriz, Tabriz, Iran
adeleh.abadi@gmail.com
Abstract— Search engine is one of the most important
tools for managing the massive amount of distributed
web content. Web spamming tries to deceive search
engines to rank some pages higher than they deserve.
Many methods have been proposed to combat web
spamming and to detect spam pages. One basic one is
using classification, i.e., learning a classification model
for classifying web pages to spam or non-spam. This
work tries to select the best feature set for classification
of web spam using imperialist competitive algorithm
and genetic algorithm. Imperialist competitive
algorithm is a novel optimization algorithm that is
inspired by socio-political process of imperialism in the
real world. Experiments are carried out on WEBSPAM-
UK2007 data set, which show feature selection
improves classification accuracy, and imperialist
competitive algorithm outperforms GA.
Index Terms— Web Spam Detection, Feature Selection,
Imperialistic Competitive Algorithm, Genetic
Algorithm
I. Introduction
With the explosive growth of information on the web,
it has become the most successful and giant distributed
computing application today. Billions of web pages are
shared by millions of organizations, universities,
researchers, etc. Web search provides great
functionality for distributing, sharing, organizing, and
retrieving the growing amount of information [1].
Search engines have become more and more important
and are used by millions of people to find necessary
information. It has become very important for a web
page, to be ranked high in the important search engines’
results. As a result many techniques are proposed to
influence ranking and improve the rank of a page. Some
of these techniques are legal and are called Search
Engine Optimization (SEO) techniques, but some are
not legal or ethical and try to deceive ranking
algorithms. These spam pages try to rank pages higher
than they deserve [2].
Web spam refers to web content that get high rank in
search engine results despite low information value.
Spamming not only misleads users, but also imposes
time and space cost to search engine crawlers and
indexers. That is why crawlers try to detect web spam
pages to avoid processing and indexing them.
Content-based spamming methods basically tailor the
contents of the text fields in HTML pages to make spam
pages more relevant to some queries. This kind of
spamming is also called term spamming. There are two
main content spamming techniques, which simply
create synthetic contents containing spam terms:
repeating some important terms and dumping many
unrelated terms [3,4].
Link spamming misuses link structure of the web to
spam pages. There are two main kinds of link
spamming. Out-link spamming tries to boost the hub
score of a page by adding out-links in it pointing to
some authoritative pages. One of the common
techniques of this kind of spamming is directory
cloning, i.e., replicating a large portion of a directory
like Yahoo! in the spam page. In-link spamming refers
to persuading other pages, especially authoritative ones,
to point to the spam page. In order to do this, a
spammer might adopt these strategies: creating a honey
pot, infiltrating a web directory, posting links on user-
generated content, participating in link exchange,
buying expired domains, and creating own spam farm
[2].
Hiding techniques are also used by spammers who
want to conceal or to hide the spamming sentences,
terms, and links so that web users do not see those [3].
Content hiding is used to make spam items invisible.
One simple method is to make the spam terms the same
color as the page background color. In cloaking, spam

62 The Impact of Feature Selection on Web Spam Detection
Copyright © 2012 MECS I.J. Intelligent Systems and Applications, 2012, 9, 61-67
web servers return an HTML document to the user and
a different document to a web crawler. In redirecting, a
spammer can hide the spammed page by automatically
redirecting the browser to another URL as soon as the
page is loaded. In two latter techniques, the spammer
can present the user with the intended content and the
search engine with spam content [5].
Various methods have been proposed to combat web
spamming and to detect spam pages. One important and
basic type of methods is considering web spam
detection as a binary classification problem [4]. In this
kind of methods, some web pages are collected as
training data and labeled as spam or non-spam by an
expert. Then, a classifier model is learned from the
training data. One can use any supervised learning
algorithm to build this model. Further, the model is used
to classify any web page to spam or non-spam. The key
issue is to design features used in learning. Ntoulas et al.
[4] propose some content-based features to detect
content spam. Link-based features are proposed for link
spam detection [6,7]. Liu et al. [8] propose some user
behavior features extracted from access logs of web
server of a page. These features depict user behavior
patterns when reaching a page (spam or non-spam).
These patterns are used to separate spam pages from
non-spam ones, regardless of spamming techniques
used. Erdelyi et al. [9] investigate the tradeoff between
feature generation and spam classification accuracy.
They conclude that more features achieve better
performance; however, the appropriate choice of the
machine learning techniques for classification is
probably more important than devising new complex
features.
Feature selection is the process of finding an optimal
subset of features that contribute significantly to the
classification. Selecting a small subset of features can
decrease the cost and the running time of a
classification system. It may also increase the
classification accuracy because irrelevant or redundant
features are removed [10]. Among the many methods
proposed for feature selection, evolutionary
optimization algorithms such as genetic algorithm (GA)
have gained a lot of attention. Genetic algorithm has
been used as an efficient feature selection method in
many applications [11,16].
In this paper, we incorporate genetic algorithm, and
imperialist competitive algorithm [12] to find an
optimal subset of features of the WEBSPAM-UK2007
data set [13,14]. The selected features are used for
classification of the WEBSPAM-UK2007 data.
The rest of the paper is organized as follows. Section
2 gives a brief introduction of the imperialist
competitive algorithm (ICA). Section 3 describes the
feature selection process by ICA and GA. Experimental
results are discussed in section 4, and finally, section 5
concludes the paper.
II. Imperialistic Competitive Algorithm
The imperialist competitive algorithm is inspired by
imperialism in the real world [12]. Imperialism is the
policy of extending the power of a country beyond its
boundaries and weakening other countries to take
control of them.
Fig. 1 Initialization of the empires: The more colonies an imperialist
possesses, the bigger is its  mark [12]
This algorithm starts with an initial society of random
generated countries. Some of the best countries are
selected to be imperialists and others are selected to be
colonies of these imperialists. The power of an empire
which is the counterpart of fitness value in genetic
algorithms, is the power of the imperialist country plus
a percentage of mean power of its colonies. Figure 1
depicts the initialization of the empires.
After assigning all countries to imperialists, and
forming empires, colonies start moving towards the
relevant imperialist (Assimilation). Then, some
countries randomly change position in the search space
(Revolution). After assimilation and revolution, a
colony may get a better position in the search space and
take control the empire (substitution for the imperialist).
Fig. 2 Imperialistic competition: The weakest colony of the weakest
empire is possessed by other empires [12]
Then, imperialistic competition begins. All empires
try to take control of the weakest colony of the weakest
empire. This competition reduces the power of weaker
empires and increases the power of the powerful ones.
Any empire that cannot compete with other empires and
increase its power or at least prevent decreasing it, will

The Impact of Feature Selection on Web Spam Detection 63
Copyright © 2012 MECS I.J. Intelligent Systems and Applications, 2012, 9, 61-67
gradually collapse. As a result, after some iterations, the
algorithm converges and only one imperialist remains
and all other countries are colonies of it. Figure 2
depicts the imperialistic competition. The more
powerful an empire is, the more likely it will take
control of the weakest colony of the weakest empire.
The pseudo code of ICA is as follows:
1. Initialize the empires
2. Assimilation: Move the colonies toward their
relevant imperialist
3. Revolution: Randomly change the
characteristics of some colonies
4. Exchange the position of a colony and
Imperialist. If a colony has more power than
that of imperialist, exchange the positions of
that colony and the imperialist
5. Compute the total power of all empires
6. Imperialistic competition: Give the weakest
colony from the weakest empire to the empire
that has the most likelihood to possess it
7. Eliminate the powerless empires
8. If there is just one empire, stop, else, go to 2
III. Feature Selection
WEBSPAM-UK2007 data set contains 96 content
based features. We use the imperialist competitive and
genetic algorithms to optimize the features that
contribute significantly to the classification.
A. Feature Selection Using ICA
In this section, the steps of feature selection using
ICA are described.
1) Initialize the empires
In the genetic algorithm, each solution to an
optimization problem is an array, called chromosome.
In ICA, this array is called country. In feature selection,
each country is an array of binary numbers. When
country[i] is 1, the ith feature is selected for
classification, and when it is 0, the ith feature is removed
[15]. Figure 3 depicts the feature representation as a
country.
1
F
2
F
3
F
…
n-1
F
n
F
Country
1
0
1
…
1
0
Feature subset =
1 3 n-1
{F ,F ,..., F }
Fig. 3 Feature representation as a country in ICA [15]
The power of each country is calculated by F-score.
F-score is a commonly used measure in machine
learning and information retrieval [3,10]. The confusion
matrix of a given a classifier is considered as table 1.
Table 1. Confusion Matrix
Classified spam
Classified non-spam
Actual spam
A
B
Actual non-spam
C
D
F-score is determined as follows
F-score = 1 / (1 / Recall + 1 / Precision) (1)
Where Recall, and Precision are defined as follows
Recall = C / (C + D) (2)
Precision = B / (B + D) (3)
The algorithm starts by randomly initializing a
population of size
pop
N
.
imp
N
of the most powerful
countries are selected as imperialists and form the
empires. The remaining countries (
col
N
) are assigned
to empires based on the power of each empire. The
normalized power of each imperialist is defined by
imp
n
1
n
N
i
i
P
NP
P



(4)
Where
n
P
is the power of
countryn
.
The initial number of colonies of
n
empire
will be
n n col
{ * }NC round NP N
(5)
To assign colonies to empires,
n
NC
of the colonies
is chosen randomly and assigned to
n
imperialist
. These
colonies along with the
n
imperialist
will form
n
empire
.
2) Assimilation
In this phase, colonies move towards the relevant
imperialist. Since feature selection is a discrete problem,
we use following operator for assimilation [15]
For each colony
 Create a binary string and assign a random
generated binary to each cell
 Copy the cells of the relevant imperialist,
corresponding to the location of ―1‖s in the
binary string, to the same positions in the colony
3) Revolution
The purpose of revolution is preserving and
introducing diversity. It allows the algorithm to avoid
local minimum. Revolution occurs according to a user
defined revolution probability. For each colony, some
cells are selected randomly and their containing binary
is inverted (―1‖ is inverted to ―0‖, and ―0‖ is inverted to
―1‖).

64 The Impact of Feature Selection on Web Spam Detection
Copyright © 2012 MECS I.J. Intelligent Systems and Applications, 2012, 9, 61-67
4) Exchange the positions of a colony and imperialist
After assimilation and revolution, a colony may gain
more power than that of imperialist. As a result, the best
colony of an empire and its imperialist exchange
positions. Then, the algorithm will continue by the
imperialist in a new position.
5) Compute the total power of empires
The total power of an empire is mainly affected by
the power of its imperialist. Another factor in
computing the total power of an empire is the power of
colonies of that empire. Of course, the main power is by
the power of the imperialist, and the power of colonies
has less impact. As a result, we define the total power of
n
empire
is defined
()
{ ( )}
nn
n
power imperialist
mean power colonies of empire
TP



(6)
Where

is a positive factor which is considered to
be less than 1. Decreasing the value of

increases the
role of the imperialist in determining the total power of
an empire and increasing it will increase the role of the
colonies.
6) Imperialistic Competition
In this important phase of the algorithm, the empires
compete to take control of the weakest colony of the
weakest empire. Each empire has a likelihood of
possessing the mentioned colony. The possession
probability of
n
empire
is obtained by
imp
1
n
n
emp N
i
i
TP
P
TP



(7)
As you can notice, the most powerful empire does
not take possession of the weakest colony of the
weakest empire, but it will be more likely to possess the
mentioned colony.
7) Eliminate the powerless empires
Imperialistic competition causes some empires to
lose power and gradually collapse. When an empire
loses all its colonies, we assume it is collapsed and
eliminate it. The imperialist of this powerless empire is
possessed by other empires as a colony.
8) Convergence
As a result of imperialistic competition and
elimination of powerless empires, the algorithm will
converge to the most powerful empire and all the
countries will be under the control of this empire. The
imperialist of this empire will determine the optimal
subset of features selected for classification, because
this imperialist is the most powerful of all countries.
B. Feature selection using GA
In the genetic algorithm, each solution to the feature
selection problem is a string of binary numbers, called
chromosome. When chromosome[i] is 1, the ith feature
is selected for classification, and when it is 0, the ith
feature is not selected [11,16].
The fitness function is considered the accuracy of the
classification model. In this research, we calculate the
fitness value of each chromosome by F-score. F-score
was described in the previous section.
The algorithm starts by randomly initializing a
population of size
pop
N
. Then, crossover and mutation
are done.
Crossover allows the generation of new
chromosomes by combining current best chromosomes.
To do crossover, single point crossover technique is
used, i.e., one crossover point is selected, binary string
from beginning of chromosome to the crossover point is
copied from one parent, the rest is copied from the
second parent. Figure 4 shows how children are
generated from each pair of chromosomes by crossover.
Mutation is similar to revolution in ICA. It maintains
genetic diversity and allows the algorithm to avoid local
minimum. To do mutation, in each chromosome, a
random cell is selected and its containing bit in inverted
(―1‖ is inverted to ―0‖, and ―0‖ is inverted to ―1‖).
Mutation and crossover occur according to a
previously defined mutation and crossover probability.
Genetic algorithm iterates for some user defined
number of generations.
Fig. 4 how children are generated from parents by crossover [17]
IV. Experimental Results
In order to investigate the impact of feature selection
on web spam classification, WEBSPAM-UK2007 data
are used. It is a publicly available web spam data
collection and is based on a crawl of the .uk domain
done in May 2007 [13, 14]. It includes 105 million
pages and over 3 billion links in 114529 hosts.
The training set contains 3849 hosts. This data set
contains content and link based features. In our
experiments, we used only content based features
because they were enough to meet our purposes. The
selected data set contains 3849 data, with 208 spam and
3641 non-spam pages. We partitioned this data set to
two disjoint sets: training data set with 2449 data, and
test data set with 1000 data. After performing feature

The Impact of Feature Selection on Web Spam Detection 65
Copyright © 2012 MECS I.J. Intelligent Systems and Applications, 2012, 9, 61-67
selection using the training set, the test set was used to
evaluate the selected subset of features.
The evaluation of the overall process was based on
weighted f-score which is a suitable measure for the
spam classification problem. It was also used as the
power function in ICA and fitness function in GA.
Bayesian Network, Decision Tree (C4.5 algorithm),
and Support Vector Machine (SVM) were chosen as
learning algorithms to perform the classification and
calculate the weighted F-score. These algorithms are
powerful learning algorithms used in many web spam
detection researches [4, 5, 18].
Following parameters were used for ICA
Number of countries = 100
Number of imperialists = 10

= 0.1
Revolution rate = 0.01
Selected parameters for GA are as follows
Initial population = 100
Number of generations (iterations) = 100
Crossover rate = 0.6
Mutation rate = 0.01
Figure 5 depicts maximum and mean power of all
imperialists versus iteration, using Decision Tress,
SVM, and Bayesian Network classifiers, in ICA. As
shown in this figure, by SVM and Decision Tree
classifiers, the global maximum of the function
(maximum power) is found in less than 5 iterations,
while by Bayesian Network, it is found in 12th iteration.
Fig. 5 Mean and maximum power of all imperialists versus iteration, using different classifiers, in ICA
Figures 6, and 7 compare ICA power function and
GA fitness function versus iteration. Figure 6 shows the
power (fitness) of best answer versus iteration
(generation), using Bayesian Network classifier, in ICA
and GA. As you can see, ICA converges faster than GA,
and has more power than GA in all iterations. Another
important point is that the initial value of f-score which
is the result of random initialization of population in
both algorithms, gets a higher increase by ICA over
iterations. This point shows that imperialistic
competition outperforms genetic evolution in the
problem of spam classification.
Fig. 6 Power (fitness) of best answer versus iteration, using Bayesian Network classifier

66 The Impact of Feature Selection on Web Spam Detection
Copyright © 2012 MECS I.J. Intelligent Systems and Applications, 2012, 9, 61-67
Fig. 7 Mean power (fitness) of all answers versus iteration, using Bayesian Network classifier
Figure 7 depicts mean power (fitness) of all answers
versus iteration, using Bayesian Network classifier, in
ICA and GA. As you can see, ICA gets a higher
increase in mean power of all answers.
The optimal subset of features selected by ICA and
GA are used to train a classification model. This model
is evaluated by the test data set. Evaluation results
obtained for Bayesian Network, Decision Tree, and
SVM classifiers are shown in table 2. These results
indicate that feature selection by both ICA and GA
techniques improves web spam classification.
Furthermore, ICA based feature selection outperforms
GA based feature selection in the problem of web spam
detection.
Table 2 The impact of ICA and GA based feature selection on web spam classification, using different classifiers
Bayesian Network
Decision Tress
SVM
Number
of
features
F-score
Number of
features
F-score
Number of
features
F-score
All features
96
0.854
96
0.935
96
0.937
GA
48
0.876
49
0.950
56
0.939
ICA
41
0.882
56
0.950
61
0.940
V. Conclusion
In this paper, we studied the impact of feature
selection on the problem of web spam classification.
Feature selection was performed by Imperialist
Competitive Algorithm and Genetic Algorithm.
Experimental results showed that selecting an optimal
subset of features increases classification accuracy, but
ICA could find better optimal answers than GA. In fact,
we observed that reducing the number of features
decreases the classification cost and increases the
classification accuracy.
Other optimization methods, such as PSO and ant
colony can be used for feature selection and compared
with ICA and GA in future works.
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KARIMPOUR Jaber (1974－), male, Tabriz, Iran,
Assistant Professor, his research directions include
verification and formal methods.
NOROOZI Ali A. (1986－), male, Tabriz, Iran, Master
of Science, his research directions include adversarial
information retrieval and distributed systems.
ABADI Adeleh (1976 - ) female, Tabriz, Iran, Master
of Science, his research directions include verification
and formal methods.