T. B. SHAHI, A. YADAV
Figure 1. The basic idea of Spam filtering.
Provider (ISP) level. However, these methods suffered
from some maintenance pro blems.
There are many efforts underway to stop the increase
of Spam that plagues almost every user on the mobile
network. Various techniques have been used to filter the
Spam messages. Naïve Bayes [1] classifier is a simple
probabilistic classifier. Its main advantage is that naïve
Bayes classifiers can be trained very efficiently in a su-
pervised learning. Naïve Bayesian classifiers are used for
parameter estimation in numerous practical applications.
In supervised learning, the parameters are estimated by
Maximum Likelihood Estimation (MLE) method. Deci-
sion Tree [2] is one of the most famous tools of decision-
making theory. Decision tr ee is a classifier in the form of
a tree structure that show s the reasoning proc ess. Suppor t
Vector Machines [3] is a linear maximal margin binary
classifier. It can be interpreted as finding a hyper-plane
in a linearly separable feature space that separates the
two classes with maximum margin—the instances closest
to the hyper-plane are known as the “support vectors” as
they support the h yper-plane on both sides of the margin.
Using these techniques, different software has been de-
veloped to filter the Spam emails. The basic concept of
these techniques is the classification of SMS or email
using trained classifier that can automatically predict if
an incoming SMS or email is Spam or legitimate. This
automatic process increases filtering performance and
provides better usability than manu al c las s ification.
Some more complex approaches were also purposed
against Spam problem. Most of them were implemented
by using machine learning methods. A Naïve Bayes al-
gorithm is used frequently which has shown a considera-
ble success in filtering Spam e-mails in English [4].
Knowledge-based and rule-based systems were also used
by researchers for English Spam filters [5,6]. SVM is
used for text classification [7], which can also be applied
for Spam filtering.
There is no work done for Nepali text SMS Spam fil-
tering yet and it is much more necessary to start the work.
The resource such as training SMS corpus is also not
available for Nepali language and the corpus used in this
work is created manually. The training corpus developed
during this study c an be made available for res earch pro-
poses.
3. Methodology: A Proposed Framework for
Spam SMS Filtering
Spam filtering engine fl o wc hart is gi ve n in Figure 2.
This describes top level data flow diagram of Spam
classification problem used in this research work. The
proposed system framework contains three steps: prepro-
cessing, feature extraction and classification.
3.1. Preprocessing
The purpose of pre-processing is to transform messages
in SMS into a uniform format that can be understood
by the learning algorithm. The first step of text mining
process is text pre-processing in which the collection of
documents is analysed syntactically or semantically.
The text message document is considered as a bag of
words because the words and their occurrences are
used to represent the document. The algorithm applied
in this stage are stemming and stop word removal,
number removal and strip whitespaces.
3.2. TF-IDF Calculation and Feature Vector
Construction
In this work, the most widely adopted feature weight-
ing scheme known as TF-IDF scheme, in Information
Retrieval (IR), TF-IDF, to represent the email as a
vector in a vector space model, and it is calculated as
Equation (1):
2
log
log
ij i
ij
kj
kk
D
tf DF
aD
tf DF
⋅
=
⋅
∑
(1)
Figure 2. Framework for Spam filtering.
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