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International Journal of Advanced Research in Engineering and Technology (IJARET)
Volume 8, Issue 6, Nov - Dec 2017, pp. 108–120, Article ID: IJARET_08_06_011
Available online at
ISSN Print: 0976-6480 and ISSN Online: 0976-6499
© IAEME Publication
Gopi Sanghani
Computer Engineering Department, Nirma University,
Ahmedabad, India
Dr. Ketan Kotecha
Parul University, Waghodia, Vadodara, India
E-mail is one of the most frequently used personal and official communication tool
over the Internet. The continually increasing ratio of spam e-mails over legitimate e-
mails and adversarial nature of spam e-mails lead to the requirement of employing
spam filter that can be updated dynamically. Moreover, the discrimination criteria of
spam and legitimate e-mails vary for different users worldwide. This leads to the
personalization of e-mail spam filter which automatically adapts individual user’s
characteristics. We propose an incremental learning model for personalized e-mail
spam filtering. We apply support vector machine - a supervised machine learning
algorithm & a discriminative classifier for the designing the classification model. We
apply incremental learning using support vector machine for the development of a
dynamically updated filter. Our model is evaluated on two different datasets that consist
of a set of e-mails structured according to the order of arrival. Experimental results
confirm the superior performance of incremental learning over the batch learning
model. The inclusion of incremental learning when the distribution of data is different
in training and testing sets helps improving classification accuracy and decreases the
false positive rate substantially.
Key words: Support Vector Machines, Incremental Training, Personalized Spam Filter,
Distribution Shift
Cite this Article: Gopi Sanghani and Dr. Ketan Kotecha, Support Vector Machine For
Personalized E-Mail Spam Filtering. International Journal of Advanced Research in
Engineering and Technology, 8(6), 2017, pp 108–120.
Classification of text is the most essential requirement in today’s era due to the increasing
volume of electronic text over the internet. The huge textual data available from different
sources over the net can be used to generate productive outcomes only when appropriate mining
or analysis tools are applied to them. Text classification is the process of distributing text
documents into two or more predefined classes depending upon the similarity measures of
Gopi Sanghani and Dr. Ketan Kotecha 109
documents. In automatic content-based text classification, the classifiers learn the class
boundary or actual distribution of each class from the training data and classify the unseen
samples from the test data. The content-based classification requires the extraction of
representative features for the transformation of unstructured text into a more explicit
structured format. The performance of such classifier relies upon how efficiently the features
are selected to represent the textual data. Moreover, the classifier selection is influenced by the
nature of the text data to be classified. It remains static when either all of the sample text is
available or the distribution of data does not change over a time. In the case when text data
collection is characterized by the inclusion of new or updated data over a time period, the
dynamic classification model is required. Classification of textual data in the non-stationary
environment remains to be a highly challenging task. It requires addressing the issues like the
modification of representative features, dynamic change in training & test data distribution,
consistently maintaining the performance of classification model etc…
Machine learning algorithms are extensively used for automatic text classification. Support
vector machine (SVM) [1, 4] is a supervised machine learning algorithm works on the
structural risk minimization principle from statistical learning theory. SVM is a discriminative
classifier that learns the decision boundary between classes and classifies unknown examples
using the learned hypothesis. Joachims [2] analyzed how the statistical properties of text
classification task and generalization performance of SVM are connected in a quantitative way.
The author presented the details of how and why SVMs can achieve good classification
performance despite the high dimensional feature space in text classification. SVM’s
robustness is improved during the learning procedure which tightens the decision boundaries
for classification [3]. SVM is a maximal margin classifier; it learns a decision boundary during
training which maximizes the distance between samples of the two classes. The design of
efficient incremental SVM learning and a detailed analysis of convergence and algorithmic
complexity of incremental SVM learning is carried out by Laskov et al. [5]. SVM’s ability to
incrementally learn with a new set of examples and support vectors is addressed by Syed et al.
In this research, we present the design and development of incremental classifier using
support vector machine for personalized e-mail classification. We analyzed the performance of
the proposed model for the classification of personalized e-mails organized in chronological
order. E-mail is the most useful and reliable tool for a business and personal communication
worldwide. The e-mail services face the inevitable downside of a number of unsolicited bulk
messages known as spam with the high circulating volume and offensive content. A huge
number of spam e-mails delivered every day regardless of the commercial or personal level of
interest that delay internet traffic and degrade many on-line services. Personalized e-mail spam
filter is the most prevalent application of automated binary text classification problems. The
binary text classification problem is defined as: Given a training set of n labeled sample e-mails
T = {t
, t
, …, t
} and C = {c
, c
} denotes e-mail categories: legitimate and spam. The task is
to learn classification model, which classifies previously unseen e-mails into one of the two
categories based on their content. Personalized e-mail spam filtering task requires addressing
two major challenges: ever increasing ratio of unwanted and useless spam e-mails and
continuously differing context and content of spam e-mails. our research work addresses these
issues by developing an incremental classifier using SVM and dynamically regenerating the
set of representative features. The rest of the paper is organized as follows: Section 2 discusses
the review of literature which provides the strong basis for our research. Section 3 describes
the details of design and development of incremental classifier using SVM. In Section 4, we
present experimental results of two real-world datasets that prove the efficiency of our system.
We conclude in Section 5 with an insight into the prospective significance of our results and
scope for future work.
Support Vector Machine For Personalized E-Mail Spam Filtering 110
E-mail spam filters use different approaches to detect spam messages and categorizing e-mails
into separate folders. Text classification task considers an approach based on the analysis of
message content of an e-mail. For a personalized e-mail spam filtering, the filter is employed
by a single user as a client side filter and messages identified as spam are usually sent to spam
folder. Filtron [7], a personalized anti-spam filter based on machine learning text categorization
paradigm, had been evaluated in real life scenario that confirmed the prominent role of machine
learning techniques for anti-spam filtering. Cheng & Li [8] proposed combined supervised and
semi-supervised classifier using SVM for personalized spam filtering. Chang, Yih, & McCann
[9] designed a light-weight user model that is highly scalable and can be easily combined with
a traditional global spam filter. A personalized spam filter is presented by Junejo & Karim [10]
using an automatic approach which built a statistical model of spam and non-spam words from
the labeled training dataset. The filter is updated in two passes over unlabeled samples taken
from individual user’s inbox.
Ghanbari & Beigy [11] proposed the algorithm called incremental RotBoost, an
incremental learning algorithm based on ensemble learning. Hsiao and Chang [12] developed
an incremental cluster-based classification method, called as ICBC. It runs in two phases. The
first phase performs clustering of e-mails in each given class into several groups, and an equal
number of features are extracted from each group. The second phase includes an incremental
learning mechanism for ICBC so that it can adapt itself to accommodate the changes of the
environment in a fast and low-cost manner. Georgala, Kosmopoulos, and Paliouras [13]
proposed active learning approach using incremental clustering for spam filtering. Taninpong
and Ngamsuriyaroj [14] proposed an incremental spam mail filtering using Naïve Bayesian
classification in which the sliding window concept is applied to keep the training set to a limited
size and the training set is updated when new emails are received.
Automatic text classification model requires a set of training samples from which the classifier
learns the statistical distribution of data. Traditional server based e-mail spam filters use
generic mail corpus for the training and then, commonly applied to a user’s inbox to
discriminate spam and legitimate e-mails. Worldwide internet users have highly dissimilar
perceptions about the definition of e-mail spam, where only global filters may not offer an
acceptable performance, as the statistical property of feature space is derived commonly [15].
The end user remains highly dependent on the discrimination of e-mails characterized by the
general training corpus. The essential advantage is users are relieved from the burden of
processing thousands of unsolicited e-mails. But only global filters cannot optimally reflect
individual user’s characteristics while discriminating e-mails. As an extensive model,
personalization of e-mail spam filtering is required which facilitate robustness and should be
adaptive to individual user’s preferences. Moreover, the content of spam e-mail changes as
spammers continuously change the manner to present the content of spam e-mails. So, there is
a need to update the filter dynamically to handle the changing distribution of representative
features. The algorithm for the proposed incremental support vector learning model for the
personalized e-mail spam filter is shown in table 1 and a detailed explanation is given in the
subsequent subsections.
3.1. Classification using SVM
SVM is a supervised machine learning algorithm essentially used for binary classification
problems. In binary classification problem, a data set X contains n labeled example vectors
, y
) . . . (x
, y
)}. Here x
represents the input vector with corresponding binary labels
Gopi Sanghani and Dr. Ketan Kotecha 111
denotes as y
ϵ {-1, 1}. Let φ(x
) be the corresponding vectors in feature space, where φ(x
) is
the implicit kernel mapping such that k(x
, x
) = φ(x
) . φ(x
) be the kernel function, implying a
dot product in the feature space. The optimization problem for a soft-margin SVM is,
  
Subject to the constraints y
(w.x + b) = 1 ξ
and ξ
0 where w is the normal vector of
the separating hyperplane in feature space and C > 0 is a regularization parameter controlling
the penalty for misclassification. Equation (1) is referred to as the primal equation. From that,
the Lagrangian form of the dual problem is:
 
#   $
Subject to 0%α
% C. This is a quadratic optimization problem that can be solved efficiently
using algorithms such as Sequential Minimal Optimization [16]. Many α
go to zero during
optimization and the remaining x
corresponding to those α
> 0 are called support vectors. If l
is the number of support vectors and α
> 0 for all i, with this formulation, the normal vector of
the separating plane w is calculated as:
 
The classification f(x) for a new sample vector x can be determined by computing the kernel
function of x with every support vector:
( )*+,"
 .# (4)
Here the bias term b is the offset of the hyperplane along its normal vector, determined
during SVM training. SVM algorithm maps input vectors into a feature space of higher
dimension and constructs an optimized hyperplane for generalization. The training samples
lying near to the hyperplane are called support vectors.
3.2. Incremental model for Personalized E-mail Spam Filter
Personalized e-mail spam filter serves as an extensive model whenever the users tend to differ
in their interest and preferences for discrimination of e-mails. Generally, user’s preferences are
influenced by their personal interests, professional profile, hobbies, etc…Many content-based
spam filters apply machine learning techniques, of which support vector machine has shown
consistently superior performance. SVM was initially applied for spam categorization by
Drucker, Wu, and Vapnik [17]. Since then various extensions and approaches based on online
and active learning have been presented by many researchers because of SVM’s good
generalization ability and higher classification accuracy.
Support Vector Machine For Personalized E-Mail Spam Filtering 112
Table 1 Personalized e-mail Spam Filter using Incremental Support Vector Learning
Training Set Trem
= {Em
ƍ threshold value for Accuracy
n training e-mails with labels
Set of Spam e-mails
Set of Legitimate e-mails
1 Pre-processing of training & Testing sets Trem
Stop word removal
2 Feature Selection
Generate a subset of Representative features FS
using Information Gain (IG) feature
selection method
Represent e-mail messages using vector space model (VSM)
i = 0
3 Build a classifier: SVM Conventional Training using SMO Algorithm (Pass I)
Output: Support Vector Set α
= {α
| k = 1 to l}
4 Testing Phase (Pass II)
Input: Testing Sets Tsem
= {Ts
, Ts
, …, Ts
// Testing instances contains set of unlabelled incoming spam and legitimate e-mails
Classify testing instances Ts
,… from Tsem
until either accuracy ƍ or FPR increases
i = i + 1
Incremental model using SVM with updated feature set (Pass III)
Resulting Support Vector Set α
Re-training set Rtrem
= α
, where Ts
is the testing instance for which
accuracy / FPR constraint is violated.
Update the set of Representative Features as follows,
= FS
Generate new subset of Features NFS
from Rtrem
Update the feature set FS
as follows:
for each distinct feature l in NFS
if IG_SCORE(feature
)) then
= FS
} &
= FS
– {feature
| feature
has lowest IG SCORE}
Retrain SVM on the re-training set Rtrem
Output Support Vector Set α
= {α
| k = 1 to l}
Repeat step 4 {Testing Phase} with k = k + 1 and step 5
Gopi Sanghani and Dr. Ketan Kotecha 113
Our classification algorithm is developed as follows: the incremental filtering process is
carried out over three passes. The first pass (Pass I in table 1) is performed using conventional
batch training of SVM, with n labeled examples, that generates the discriminant function F(x).
Pass II comprises a series of testing phases in which small batches of incoming unlabeled e-
mails are given to identify true labels. Pass III is carried out by activating incremental training
whenever any one of the two performance criteria – accuracy and false positive (FP) rate are
violated. Whenever an accuracy of the filter decreases below some threshold or the false
positive rate increases, the incremental training will be initiated. Accuracy decreases when
miss-classified mail ratio increases, which indicates that the re-training is required to upgrade
the filter performance. False positive (FP) rate is also more sensitive because the higher rate of
recognizing non-spam messages as spam would definitely degrade the filter performance.
Decreasing accuracy or increasing FP rate is the indication of an increase in the error of
classification model. A substantial increase in the error of the algorithm because of a change
in the class distribution signifies that the current decision model has become less effective over
a certain period of time.
An important property SVM possesses is, a set of support vectors represents the feature
space and class boundaries in a very concise manner. So, incremental SVM can be trained by
preserving support vectors and adding them to the next batch of incoming examples. Initially,
conventional batch training is conducted with the substantial number of labeled e-mails
representing both spam and legitimate categories. In case of personalized spam e-mails, certain
types of spam e-mails appear for a short duration of time while some of the spam e-mails appear
regularly. The statistical properties of training dataset and testing datasets differ whenever a
class or data distribution changes. Either the target concept or the data distribution change over
a time, which may often lead to the necessity of revising the current model. Individual user’s
preferences for unwanted messages may remain same over a long period of time; the relative
frequency of different types of spam may change drastically with time. So, before activating
incremental training feature space is updated dynamically to include new features with higher
discriminating ability. The feature set is updated using a heuristic function which re-calculates
feature’s information gain score from re-training set before activating the incremental training.
Modifying the feature set would enable the classifier to effectively re-learn the updated
distribution of data. The change in the content of e-mails or change in the preferences of user
apparently requires updating the filter dynamically. During incremental training, the true label
of e-mails is provided in order to correctly derive the modified hypothesis function. The re-
training results in modified discriminant function which enables the classifier to handle the
distribution shift proficiently.
We have applied Sequential Minimal Optimization (SMO), a special case of decomposition
method for SVM training. The SMO algorithm is designed to avoid the large quadratic
optimization problem that is required to solve for the implementation of SVM classification
model. At every step, the SMO algorithm analytically solves a QP sub-problem for the two
chosen Lagrange multipliers and updates the SVM model accordingly. SMO maintains kernel
matrix of size equal to the total number of samples in the dataset, which allows it to handle
very large training sets. Incremental SMO learning is achieved by keeping the old α value of
support vectors and setting α value to zero for new examples.
4.1. Dataset and Experiments
To validate the accuracy of the proposed incremental model, we use two different datasets
Enron [18] and ECUE [19]. Both the benchmark datasets contain the personalized collection
Support Vector Machine For Personalized E-Mail Spam Filtering 114
of e-mails. Another essential property the datasets possesses is, the e-mails are as per the order
of arrival which perfectly suits the evaluation of our incremental model. The first well-known
dataset Enron contains six large personalized folders of spam and legitimate e-mails. This
dataset contains pre-processed e-mail messages with the removal of attachments. The dataset
belongs to six e-mail directories farmer-d, kaminski-v, kitchen-l, williams-w3, beck-s, and
lokay-m, named as Enron1 to Enron6. Text pre-processing tasks such as tokenization, stop
word removal and stemming are performed using Rapid Miner [20] prior to applying filtering
process. Processed e-mails are represented using Vector Space Model (VSM) in which each e-
mail is represented by n dimension vector using binary representation. In three of the folders,
a legitimate-spam ratio is approximately 3:1, while in the other three the ratio is inverted to
1:3. The total number of messages in each dataset is between five and six thousand. SVM is
trained on a set of e-mails taken from individual user’s inbox to capture personalization. In the
simulation run, SVM is initially trained with an approximately 33% e-mails including
legitimate and spam both. Remaining e-mails are used to create incoming testing instances. As
the dataset contains chronologically sorted e-mails, ten different testing instances of equal size
are created. The distribution of e-mails for training and testing sets is given in table 2 for Enron
dataset and in table 3 for ECUE dataset.
Table 2 Enron Dataset Distribution
Dataset Training E-mails Testing E-mails
Legitimate Spam Legitimate Spam
ENRON1 1224 500 1000 2448
ENRON2 1100 400 1096 3261
ENRON3 1300 500 1000 2712
ENRON4 500 1000 3500 1000
ENRON5 500 1225 2450 1000
ENRON6 500 1000 3500 1000
Table 3 ECUE Test Dataset Distribution
Legitimate Spam Legitimate Spam
Feb‘03 -- -- 151 142
Mar 93 629 56 391
Apr 228 314 144 405
May 102 216 234 459
Jun 89 925 128 406
Jul 50 917 19 476
Aug 71 1065 30 582
Sep 145 1225 182 1849
Oct 103 1205 123 1746
Nov 85 1830 113 1300
Dec 105 576 99 954
Jan ‘04 -- -- 130 746
The ECUE 1 and ECUE 2 datasets are taken from the ECUE concept drift 1 and 2 datasets,
respectively. Each dataset is a collection of e-mails received by the individual user over the
period of 10 to 12 months. These dataset contains three types of features: (a) word features, (b)
letter or single character features, and (c) structural features, e.g., the proportion of uppercase
Gopi Sanghani and Dr. Ketan Kotecha 115
or lowercase characters. In this dataset, a separate set of training e-mails is given which
contains 500 spam and 500 legitimate e-mails. And testing e-mails contain a total of
approximately 10,000 e-mails that are separated month wise as shown in table 3. The
organization of Enron and ECUE allow our incremental model to retrain the classification
model using a small set of new examples in order to update the filter dynamically whenever
validation criteria are violated.
4.2. Performance Measures
The filter is evaluated with well-known performance measures used in classification. We
measure accuracy, false positive rate and false negative rate defined as:
01123415 
 9
< =
Where, NS and NL are total spam and legitimate e-mails, nll and nss are the numbers of
legitimate and spam e-mails classified correctly and nls and nsl are the numbers of
legitimate and spam e-mails not correctly classified. The other two success measures employed
are micro-F1 and macro-F1 defined as:
K13B  EL $ M F M GF  GN
K413B  EL  O E
$ M F M G F  GS
Where P and R denote precision and recall measures given as:
 
3@14>>G 
 
4.3. Results and discussion
In this research, an experiment is carried out with an aim of analyzing and comparing the
performance of batch learning model with incremental learning using SVM in dynamic feature
space. Also, we analyze the performance of incremental learning model in the presence of
distribution shift. Generally, training data defines distribution and derives discriminant
function, which performs well when testing data follows the same distribution. In the case of a
distribution shift, the discriminant function has to be updated to maintain and improve the
performance. Moreover, the changes in data distribution causes the requirement of modifying
the set of representative features to precisely define the class boundary. The selection of
representative features is done with Information Gain [21] feature selection method.
Table 4 shows accuracy, precision, and recall, the most common performance measures in
binary classification for Enron and ECUE datasets. The precise comparison results of batch
training and incremental training models clearly indicates the superior performance of the
incremental model in all the cases. Fig. 1 shows the comparison graph for the accuracy
achieved for both the models. As the testing data is sorted chronologically, we have created ten
testing instances in Enron dataset. In ECUE, the testing instances are already given month wise.
We observe that in the case of conventional SVM training, the accuracy decreases from the
testing set TS1 to TS10. In conventional training, SVM is trained initially once only. E-mail
datasets are sorted as per arrival order so we can say that over a period of time due to the
changing nature of e-mails the classification model becomes ineffective. The classification
model consistently performs well by improving the accuracy level using incremental re-
Support Vector Machine For Personalized E-Mail Spam Filtering 116
training. The accuracy is averaged over all testing instances. Updating the set of representative
features before re-training SVM allows the classification model to relearn the modified
distribution of data.
Table 4 The classification results for Personalized e-mail spam filtering
Datasets Inbox SVM Incremental Training
with updated Feature Set SVM Batch Training
Accuracy Precision Recall Accuracy Precision Recall
ENRON1 96.28 0.97 0.95 94.18 0.95 0.93
ENRON2 96.01 0.98 0.90 87.90 0.93 0.62
ENRON3 96.76 0.98 0.93 93.83 0.96 0.89
ENRON4 98.19 0.98 0.98 96.49 0.98 0.96
ENRON5 97.45 0.94 0.99 96.14 0.90 0.99
ENRON6 96.86 0.94 0.98 91.58 0.92 0.92
ECUE ECUE 1 97.32 0.99 0.96 91.63 0.98 0.92
ECUE 2 96.52 0.99 0.92 86.18 0.99 0.85
Figure 1 Accuracy Comparison for Batch and Incremental Learning model
Fig. 2 shows FPR comparison for both the learning models. Incorrect classification of
legitimate e-mails as spam i.e. the occurrence of False Positives (FP) degrades the filter
performance. An FP is significantly more harmful than a False Negative (FN) i.e. a spam e-
mail incorrectly classified as legitimate. Fig. 3 shows the comparison of the false negative rate.
Fig. 4 shows ROC curve for the comparison of true positive rate (TPR) vs. false positive rate
(FPR). Classification results show that incremental training of SVM allows obtaining and
substantially improving the accuracy of the filter. Moreover, the average FPR achieved in the
incremental model is decreased by 34% as compared to the average FPR in the batch model.
Fig. 5 and 6 show the comparison of Micro F1 measure and Macro F1 measures achieved in
both Enron and ECUE datasets respectively. The incremental model successfully handles the
change of data distribution and improve the filter performance noticeably.
Gopi Sanghani and Dr. Ketan Kotecha 117
Figure 2 False Positive Rate Comparison for Batch and Incremental Learning model
Figure 3 False Negative Rate Comparison for Batch and Incremental Learning model
Figure 4 ROC Comparison for Batch and Incremental Learning model
Support Vector Machine For Personalized E-Mail Spam Filtering 118
Figure 5 Micro F1 Comparison for Batch and Incremental Learning model
Figure 6 Macro F1 Comparison for Batch and Incremental Learning model
E-mail spam filtering on a personalized level has been one of the most challenging
classification tasks in the presence of distribution shift. In this paper, we describe the design,
development and evaluation of a personalized e-mail spam filter using incremental training of
support vector machine. We apply the technique for the modification of representative features
before initiating the incremental training. The experimental outcomes show that the
incremental learning effectively helps the classification model to re-learn the modified class
boundary information. Hence, this validates the purpose of the application by successfully
addressing two major issues of personalized e-mail spam filtering i.e. changing user
preferences and distribution shift. SVM incremental learning algorithm outperforms the batch
model and the false positive rate is decreased by 34%. The results confirm the applicability of
our unique approach that focuses on the incremental learning of SVM with the heuristically
updating feature set for the improvement of the classification model. The future work addresses
to resolve the issue of dynamic changes in the set of representative features for the prediction
of a distribution shift.
Gopi Sanghani and Dr. Ketan Kotecha 119
We are grateful to the Nirma University for providing resources and other facilities to carry out
this research work.
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