Evolutionary Symbiotic Feature Selection for Email Spam Detection
Paulo Cortez
1
, Rui Vaz
2
, Miguel Rocha
3
, Miguel Rio
4
and Pedro Sousa
5
1
Centro Algoritmi, Dep. Information Systems, Universidade do Minho, Guimar
˜
aes, Portugal
2
Dep. Information Systems, Universidade do Minho, Guimar
˜
aes, Portugal
3
CCTC, Dep. of Informatics, Universidade do Minho, Braga, Portugal
4
Dep. Electric and Electronic Engineering, University College London, Torrington Place, London, U.K.
5
Centro Algoritmi, Dep. of Informatics, Universidade do Minho, Braga, Portugal
Keywords:
Collaborative Filtering, Content-based Filtering, Evolutionary Algorithms, Feature Selection, Naive Bayes,
Spam Email, Symbiotic Filtering, Text Classification.
Abstract:
This work presents a symbiotic filtering approach enabling the exchange of relevant word features among
different users in order to improve local anti-spam filters. The local spam filtering is based on a Content-
Based Filtering strategy, where word frequencies are fed into a Naive Bayes learner. Several Evolutionary
Algorithms are explored for feature selection, including the proposed symbiotic exchange of the most relevant
features among different users. The experiments were conducted using a novel corpus based on the well known
Enron datasets mixed with recent spam. The obtained results show that the symbiotic approach is competitive.
1 INTRODUCTION
Spam messages are an intrusion of privacy, with prob-
lematic content, such as online fraud, phishing attacks
or viruses. Due to its tiny cost to reach a high number
of potential consumers, spam is widely spread. Cur-
rently, there are two main approaches to fight spam
(M
´
endez et al., 2008): Collaborative Filtering (CF)
and Content-Based Filtering (CBF). CF strategies are
based in sharing information about spam messages
(spam message hash, spammer IP address, etc.) in
a community of users. CBF filters uses a data min-
ing classifier to analyse content (e.g., word frequen-
cies) extracted from email messages. However, CF
often suffers from sparsity of data, when users clas-
sify very few messages, and first-rater problem, where
an e-mail cannot be classified unless a user has rated
it before. Also, people have personal views of what
is spam and CF often discards this issue (Gray and
Haahr, 2004). On the other hand, CBF requires sev-
eral representative training examples and poor perfor-
mances are often achieved for new users.
Recently, a novel Symbiotic Filtering (SF) ap-
proach was proposed, which makes use of useful ca-
pabilities from both CF and CBF (Lopes et al., 2011).
Under the Web 2.0 paradigm, the idea is to use the In-
ternet to gather distinct users interested on similar but
not identical goals, i.e., improve the spam detection at
a personalized level. The aim of SF is to foster mu-
tual relationships, where all or most members bene-
fit. Rather than exchanging data that may be sensitive
(e.g., normal ham messages), the goal of SF is to share
information about what each local CBF has learned.
Within SF there are two interesting sharing possibili-
ties: CBF models or relevant features. The former ap-
proach was addressed in (Lopes et al., 2011). This pa-
per focuses on the latter approach, which is less sensi-
tive, since no spam/ham probability is associated with
a particular feature, and requires less communication
overhead. For feature selection methods we propose
the use of Evolutionary Algorithms (EAs) (De Jong,
2006), since they perform a global multi-point search,
quickly locating areas of high quality, even when the
search space is very complex. We compared the pro-
posed evolutionary SF approach with other non shar-
ing EA variants, as well with a CBF filter that uses a
simpler information gain feature selection method.
The related work of this paper is presented in Sec-
tion 2. Section 3 presents the e-mail data, local and
symbiotic filtering methods, and evaluation metrics.
Next, the results are presented and discussed (Section
4). Finally, closing conclusions are drawn (Section 5).
159
Cortez P., Vaz R., Rocha M., Rio M. and Sousa P..
Evolutionary Symbiotic Feature Selection for Email Spam Detection.
DOI: 10.5220/0004010201590164
In Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics (ICINCO-2012), pages 159-164
ISBN: 978-989-8565-21-1
Copyright
c
2012 SCITEPRESS (Science and Technology Publications, Lda.)
2 RELATED WORK
Within the CBF approach to fight spam, the Na
¨
ıve
Bayes (NB) classifier is the most popular learning
algorithm, since it is very fast while often achiev-
ing high detection accuracies (Garriss et al., 2006).
Most NB solutions are based on textual content (i.e.
word frequencies) of email messages. This popu-
lar approach has the advantage of being generaliz-
able to wider contexts, such as spam instant messag-
ing (spim) detection. However, the CBF performance
is dependent of the type of feature selection method
used. In (M
´
endez et al., 2008) five well-known fil-
ter feature selection methods were combined with
four types of Na
¨
ıve Bayes classifiers, with the results
showing that the choice of the correct feature selec-
tion method is a key issue to improve spam detection.
In (Lopez-Herrera et al., 2008), multi-objective
EAs were used to achieve a set of filtering rules with
different profiles. The filtering rules were encoded
as expression syntax trees and the Non-dominated
Sorting Genetic Algorithm (NSGA-II) was applied to
maximize two evaluation criteria, i.e., precision and
recall. In the same year, (Dudley et al., 2008) pro-
posed an EA to analyse different configurations for
SpamAssassin, a widely-used open source spam filter.
Their approach consisted in using an EA to achieve
an optimal setup, at a personalized level, for the set
of weights that is used to infer if a given message is
spam. In this case, the EA minimized the number
of false positives and false negatives. (Zhang et al.,
2008) describes a genetic programming approach to
feature extraction for a cost-sensitive classification
task of spam. The fitness used comprised three objec-
tives: an approximation to the bayes error, misclas-
sification cost and number of tree nodes used to en-
code a particular solution. The solution proposed in
(Zhang et al., 2008) is the most analogous to the one
presented in this paper, since an EA is used for the
feature selection. However, our approach makes use
of a novel collaborative approach, i.e., sharing rele-
vant features among multiple users.
3 MATERIALS AND METHODS
Spam Data: While there are several public bench-
mark datasets created to evaluate anti-spam filters,
most of these datasets are not fitted for personalized
filtering (Metsis et al., 2006). To evaluate SF, ide-
ally there should be real mailboxes collected from
distinct users (possibly from a social network) dur-
ing a given time period. Yet, due to logistic and pri-
vacy issues, it is quite difficult to obtain such data.
Therefore, we created a novel corpus based on a real-
ist and synthetic mixture of real ham and spam mes-
sages. The ham messages are from the popular En-
ron email collection, related with the year of 2001.
From the total of 158 Enron users, we selected the
five users that had an higher time overlap: martin-
t, platter-p, saibi-e, scholtes-d and smith-m. Since
these employees worked at the same organization,
it is reasonable to assume that they could be some-
how connected in the context of a professional social
network. The spam set consists in 19196 messages
that were retrieved from the Bruce Guenter collection
(http://untroubled.org/spam/), which is based
in fake emails published in the Web, during the year
of 2010 (our dataset was built in 2011). Only mes-
sages with Latin character sets were selected, because
the ham messages use this type of character coding
and non-Latin mails would be easy to detect. As pro-
posed in (Lopes et al., 2011), the mixture of spam and
ham is based on the time each message was received.
First, 9 years were added to the date field of all ham
emails. Then, for each user, a random spam/ham ra-
tio, uniform within [0.5,3], was initially set. Next, the
corresponding amount of spam messages were ran-
domly selected, within the same time period as de-
fined by the ham data, from the whole spam set and
mixed with the ham data. While the overall spam/ham
ratio is set by a random and fixed value, it should be
noted that under the proposed time ordered mixture,
the spam/ham ratios fluctuate through time. Table 1
shows a summary of the adopted corpus.
Table 1: Summary of the SF corpus.
user size features time period spam/ham
mar 888 5057 [3/10,12/10] 1.51
pla 672 2303 [4/10,12/10] 2.15
sai 1688 4476 [3/10,12/10] 1.38
sch 765 2833 [4/10,12/10] 1.50
smi 941 3460 [3/10,12/10] 0.94
Evaluation: Since spam detection evolves through
time (i.e. there is a concept drift), we will adopt the
more realistic incremental retraining evaluation pro-
cedure, where a mailbox is split into batches b
1
,..., b
n
of K adjacent messages (|b
n
| may be less than K)
(Metsis et al., 2006). For i ∈ {2, ...,n − 1}, the spam
filter is trained with D
u
= b
1
∪ .. . ∪ b
i
and tested with
the messages from b
i+1
, where D
u
denotes the train-
ing data for user u (Fig. 1). It should be noted that
the minimum D
u
size is set to 2K, since the EA algo-
rithms use the last batch of the training data as the
validation set, to compute the fitness value. For a
given probabilistic filter, the predicted class for mes-
sage x
j
is given by: spam if p(spam|x
j
,D
u
) > D,
ICINCO2012-9thInternationalConferenceonInformaticsinControl,AutomationandRobotics
160
where D ∈ [0.0, 1.0] is a decision threshold. For a
given D and test set, it is possible to compute the
true (T PR) and false (FPR) positive rates: T PR =
T P/(T P + FN) and FPR = FP/(T N + FP), where
T P, FP, T N and FN denote the number of true pos-
itives, false positives, true negatives and false nega-
tives, respectively. The receiver operating character-
istic (ROC) curve shows the performance of a two
class classifier across the range of possible thresh-
old (D) values, plotting FPR (x-axis) versus T PR (y-
axis) (Fawcett, 2006). The global accuracy is given
by the area under the curve (AUC =
R
1
0
ROCdD) met-
ric, which is adopted in this paper for the evalua-
tion of the distinct spam detection methods. With
the incremental retraining procedure, one ROC will
be computed for each b
i+1
batch and the overall re-
sults will be presented by adopting an average of
the AUC values computed for each batch. In case
of the EA algorithms, several runs are executed for
each method. Confidence intervals are given by the
t-student test at the 95% confidence level, while sta-
tistical significance is measured using non-parametric
Mann-Whitney paired tests (Flexer, 1996).
val. set
|b |
b b b
1 2 3
batch
b
n
...
mailbox
K
K
K
K
test set
test set
ts. set
......
runs
n−2
2
1
training data
training data
training data
n
Figure 1: Example of the incremental retraining procedure.
Content-Based Filtering: The CBF filter adopted
uses only textual content, i.e. word frequencies of
email messages, and is based on the popular NB clas-
sifier. The preprocessing follows the steps proposed
in (Metsis et al., 2006). The word frequencies were
extracted from the subject and body of the message.
All HTML tags and non numeric or alphabetic char-
acters were removed. Then, all capital characters
were converted into lowercase letters. Next, words
with two ore less characters were removed from the
text. Each message j was then encoded into a vector
x
j
= (x
1 j
,. .. ,x
m j
), where x
i j
is the number of occur-
rences of token X
i
in the text. As an initial feature se-
lection, any words with very small frequency (x
i j
< 5)
in the whole mailbox were removed. In Table 1, the
column features denotes the total number of distinct
words present in each mailbox of the analyzed cor-
pus.
For the simpler CBF, the feature selection method
is based on the information gain criterion (M
´
endez
et al., 2008), which is applied to the training set in or-
der to select the F
IG
most relevant features. Given its
popularity for spam filtering, there are several NB ver-
sions that have been successfully applied within this
domain (Metsis et al., 2006). In this paper, we adopt
the Multinomial NB variant, as implemented in the
popular open source RapidMiner tool (Mierswa et al.,
2006) and when using a sparse representation, which
heavily reduces the computational memory require-
ments. In (Metsis et al., 2006), such variant obtained a
high quality spam detection accuracy, outperforming
other NB versions, such as the Multivariate Gaussian.
Evolutionary Feature Selection: Generally there are
two main methods for feature selection: filters and
wrappers (Guyon and Elisseeff, 2003). Filters meth-
ods are independent of the learning algorithm and are
applied in the preprocessing stage (e.g., Information
Gain). Wrappers test several combinations of fea-
tures and each testing requires the training of a given
classifier. Wrapper methods tend to be more accu-
rate than filters, although they require more computa-
tion and the results are specific to a particular classi-
fier. The evolutionary approach for feature selection
adopts the same CBF filter previously described. In
order to reduce the search space to a reasonable size,
the information gain filter is first applied to the train-
ing data, in order to select the F
IG
most relevant fea-
tures. Then, an EA is applied as a wrapper method,
requiring the training of several NB classifiers. Each
EA individual is represented as a variable-sized set
of strings, which allows the definition of a maximum
and minimum number of words. This representation
is a more natural form that is closer to the problem
to be solved and has the advantage of not requiring a
mapping function, when compared with the popular
binary representation, since each individual contains
the explicit words used by the CBF. The EAs are set
to maximize the AUC metric. The computation of the
respective fitness is obtained as follows. For a given
run of the incremental training (Fig. 1), the training
data is divided into training (with all cases except the
validation samples) and validation sets (with the last
K emails). The features that appear in a given chro-
mosome are fed into the CBF model, which is fit using
all training samples. The NB predictions over the val-
idation set are then used to compute the AUC value.
After the EA termination criteria, the best individual
is selected and the respective features are used to feed
a new NB that is fit by using all training data.
For the EA engine, we adopted a general EA, as
implemented in the JECoLi Java library (Evangelista
et al., 2009). First, there is an initial population with
P individuals. New solutions are bred through the use
of random respectful recombination (Radcliffe, 1993)
EvolutionarySymbioticFeatureSelectionforEmailSpamDetection
161
and random mutation operators. The recombination
method creates two lists of features: first, with com-
mon features between the two progenitors and; sec-
ond, with the remaining features. The descendants
contain all features from the first set plus a random
number of words from the second list. The mutation
operator replaces a random number of features from
the chromosome. In both operators, the minimum and
maximum number of features is always preserved.
The genetic operators are used (with 50% probability
each) to create a new population of size P. Both the
original and new populations are evaluated and then
a tournament selection is adopted (with a tournament
size of 2) to select the P individuals that will survive
to the next generation. Finally, the EA is stopped af-
ter G generations. Figure 2 shows the schematic of
the EA engine adopted. Each EA is executed n − 2
times, according to the incremental training approach,
where each EA run is applied over the training data
available at the i-th iteration of the incremental proce-
dure. When creating a random population, P individ-
uals are generated, such that each individual contains
a random size, between the minimum and maximum
threshold, with randomly selected words from the set
of F
IG
features. Two local EA variants were explored,
which are dependent on the type of initial population
used. The EA with reinitialization (EAR) uses a ran-
dom initial population for each run of the incremental
training procedure, thus reseting past optimizations.
In contrast, the EA with memory (EAM) only uses a
random population in the first iteration of the incre-
mental batch (i.e., when the training set is equal to
b
1
). When a new batch of messages is included in the
analysis, the EA restarts with the last population.
New Population
Genetic
Recombination
Selection
Recombination
Evaluation
Feature Selection
Mutation
Initial Population
Respectful
NB training
AUC
Figure 2: Schematic of the EA engine.
Symbiotic Filtering: The EA that performs a SF
(EAS) assumes a symbiotic collaboration within a
group of distinct users, which share the most relevant
features among the group. Given that no spam or ham
probability is assigned to these features, this sharing
does not arise privacy concerns. Still, if needed, an
anonymous distribution of features can be set, under
the use of a trustable application or secure server, as
described in (Lopes et al., 2011). The EAS works
similarly to EAM except that the initial population
includes a percentage of p
s
individuals, with features
shared from other users, and 1 − p
s
of the best indi-
viduals from the previous EAS batch. It is assumed
that the symbiotic group has a size of n and each user
runs a EAS and during the same time period. To re-
duce communication costs and computational effort,
the exchange of features is asynchronous and occurs
only when a new CBF is trained. In this paper, this oc-
curs every time a new batch of messages is analyzed.
It should be noted that while the same batch size of K
messages is used for all users, the messages included
in each batch may be related with distinct dates.
To respect the chronological order of the distinct
EAS, the last message date of the training set (t) is
used to synchronize the exchange of features. Thus,
the sharing is performed among the best individu-
als from the distinct EAS that were available at time
t. For each iteration of the incremental retraining, a
given user receives a total of S = p
s
× P individuals,
such that the S solutions are equitably retrieved from
the other members of the symbiotic group (i.e. each
user shares S/(n − 1) individuals). To simulate the
distributed execution of the EAS, the JECoLi library
was adapted to include a different thread for each user.
The distinct threads were synchronized, in order to
preserve the temporal order. In some situations, user
A may receive external features from user B that are
not included in the mailbox of A (i.e., mapped in the
matrix of word frequencies of A). To increase the di-
versity of the shared features, we opted for search-
ing for additional features that are extracted from the
the best individuals from user B and that appear in
the mailbox of A. This procedure is executed until
the number of exchanged features is equivalent to the
ones contained in the desired S/(n − 1) individuals
exchange. For demonstration purposes, Fig. 3 plots
an example of the symbiotic exchange of individuals.
In the example, two users (A, B) share S/2 individu-
als each with user C. It should be noted that while the
distinct EAS are run within the same time period, the
exchange of individuals is performed using different
EAS evolution stages. In the example, the best indi-
viduals from user A were searched using all data until
batch 3, while the exchange from B was performed
over a EA that included only batches 1 and 2.
4 EXPERIMENTS AND RESULTS
All experiments were conducted in Java program-
ming enviroments and we set K = 100 for all users,
ICINCO2012-9thInternationalConferenceonInformaticsinControl,AutomationandRobotics
162
time
b3
t
b1 b2 b3
b2
S/2
b1
b2
b3
S/2
b1
user A
user B
user C
mailboxes
Figure 3: Example of time ordered exchange of individuals.
a reasonable value also adopted in (Metsis et al.,
2006)(Lopes et al., 2011). For each iteration of the
incremental training, the number of information gain
selected features was set F
IG
= 500. The configura-
tion parameters used by the EA versions are listed in
Table 2. The values related with the last two rows
are only used by the EAS. Each EA algorithm was
executed a total of 10 runs and results are presented
as the average of these runs. We start the analysis
by considering the user mar. The results obtained for
each batch are presented in Table 3. In general, the
obtained results favor the symbiotic approach (EAS),
which outperforms the non sharing EA variants (EAR
and EAM) and the local NB filter (CBF). In effect, the
last row of Table 3 presents the average AUC value
over all batches and the higher average AUC value is
achieved for EAS. For this user, the remaining meth-
ods (EAR, EAM, CBF) achieve considerable worst
performances for two of the analyzed batches (5, 9).
Table 2: Parameters set for the EA methods.
parameter value
population size (P) 20
minimum individual size (#features) 300
maximum individual size (#features) 400
elitism value 2
stopping criterion (G) 100
shared percentage (p
s
) 0.6
symbiotic group size (n) 5
The global results are measured using two criteria:
average AUC value, over of all batches (b
i
), shown in
Table 4; and percentage of test set batches where the
method returns the best AUC value (Table 5). For
each user, the last criterion is computed using the for-
mula w/n
ts
, where w denotes the number of wins of
the method and n
ts
the number of test set batches.
When two methods produce the same best AUC value
(e.g., batch 3 for user mar as shown in Table 3), the
value of w is increased with 0.5, for each tie and both
methods. Overall, the best method is the symbiotic
EA (EAS). In terms of the average AUC value, it is the
Table 3: Results for user mar (AUC values, best in bold).
b
i
CBF EAR EAM EAS
3 0.960 0.971±0.006 0.969±0.006 0.971±0.005
4 0.955 0.957±0.008 0.955±0.011 0.956±0.019
5 0.919 0.921±0.006 0.923±0.005 0.953±0.012
?
6 0.973 0.980±0.004 0.974±0.006 0.980±0.004
7 0.974 0.985±0.004 0.986±0.001 0.980±0.005
8 0.963 0.958±0.008 0.953±0.008 0.976±0.010
?
9 0.877 0.919±0.009 0.935±0.007 0.971±0.007
?
b
i
0.946 0.956 0.956 0.970
? - statistically significant when compared with EAM, EAR and CBF.
best option for four users and obtains the higher mean
value (over all users), as observed in Table 4. More-
over, EAS presents the highest percentage of wins for
three users and the best mean value (last row of Ta-
ble 5). Regarding the non sharing EAs, EAR and
EAM obtain a similar performance, in terms of the
mean (over all users) AUC value. Nevertheless, EAM
presents the second best mean percentage of wins.
Table 4: Results for all users (AUC values, best in bold).
user CBF EAR EAM EAS
mar 0.946 0.956±0.006 0.956±0.006 0.970±0.009
†
pla 0.950 0.949±0.007 0.947±0.007 0.953±0.010
sai 0.983 0.975±0.006 0.980±0.005 0.974±0.011
sch 0.961 0.967±0.004 0.964±0.005 0.970±0.010
smi 0.935 0.938±0.010 0.942±0.006 0.943±0.010
†
mean 0.955 0.957 0.958 0.962
† - statistically significant when compared with CBF.
Table 5: Percentage of batch wins (best value in bold).
user CBF EAR EAM EAS
mar 0.0 28.5 14.3 57.1
pla 20.0 0.0 20.0 60.0
sai 38.7 9.7 25.8 25.8
sch 41.7 8.3 0.0 50.0
smi 0.0 18.8 43.8 37.5
mean 20.1 13.1 20.8 46.1
Globally, all spam detection methods achieve a
high quality spam detection, with all average AUC
values higher than 0.9. The differences between the
distinct methods may seem small, with improvements
of 0.3 to 2.4 pp of EAS over CBF. Nevertheless, it
should be noted that higher improvements may be
achieved for a particular batch. For example, the
difference between EAS and CBF for user mar and
batch 9 is 9.4 pp (Table 3). Also, as shown in Table 5,
EAS tends to provide the best AUC values in most
of the batches. Moreover, even small improvements
may lead to a considerable user added value, since it
translates into a better spam email detection proba-
bility, which means less time reading unwanted mes-
EvolutionarySymbioticFeatureSelectionforEmailSpamDetection
163
sages and more immunity to virus, worms or phish-
ing attacks. EAS requires more communication and
computation when compared with the simpler CBF
method. However, the increase in computation is still
affordable for a common user and the communication
costs are low, around the size of one email message
for every batch (e.g. 100 messages). Moreover, the
execution of a batch for the EA is not computationally
expensive. For example, under the tested computer,
the average execution times for 100 generations of the
EAS were 11s for user pla and 41s for user mar.
5 CONCLUSIONS
This paper proposes a novel distributed feature selec-
tion approach for spam detection making use of a EA
engine for the search of the best features and adopts
a SF stategy to share features among distinct users.
The goal is to reuse features that were considered rel-
evant for other users in order to improve spam detec-
tion at a personalized level. The NB classifier was
adopted as the local CBF and tested in a new cor-
pus that performs a realistic mixture of ham messages
from five Enron users with recent spam. The perfor-
mance of EAS was compared with two local EA algo-
rithms (EAR and EAM), as well as the simpler CBF
method based on the information gain criterion. The
results show that even considering a small simbiotic
group (i.e. 5 users), EAS achieves the best spam de-
tection performance, as measured by the AUC metric.
ACKNOWLEDGEMENTS
The work of P. Cortez and P. Sousa was funded
by FEDER, through the program COMPETE and
Portuguese Foundation for Science and Technology
(FCT), by project FCOMP-01-0124-FEDER-022674.
REFERENCES
De Jong, K. (2006). Evolutionary computation: a Unified
Approach. The MIT Press.
Dudley, J., Barone, L., and While, L. (2008). Multi-
objective spam filtering using an evolutionary algo-
rithm, pages 123–130. IEEE.
Evangelista, P., Maia, P., and Rocha, M. (2009). Implement-
ing metaheuristic optimization algorithms with jecoli.
In Intelligent Systems Design and Applications, 2009.
ISDA’09. Ninth International Conference on, pages
505–510. IEEE.
Fawcett, T. (2006). An introduction to ROC analysis. Pat-
tern Recognition Letters, 27:861–874.
Flexer, A. (1996). Statistical Evaluation of Neural Networks
Experiments: Minimum Requirements and Current
Practice. In Proc. of the 13th European Meeting on
Cybernetics and Systems Research, volume 2, pages
1005–1008, Vienna, Austria.
Garriss, S., Kaminsky, M., Freedman, M., Karp, B.,
Mazi
`
eres, D., and Yu, H. (2006). RE: reliable email.
In Proc. of the 3rd conference on Networked Systems
Design and Implementation (NSDI), pages 297–310,
San Jose, CA. USENIX Association Berkeley, USA.
Gray, A. and Haahr, M. (2004). Personalised, Collaborative
Spam Filtering. In 1st Conference on E-Mail and Anti-
Spam CEAS.
Guyon, I. and Elisseeff, A. (2003). An introduction to vari-
able and feature selection. Journal of Machine Learn-
ing Research, 3:1157–1182.
Lopes, C., Cortez, P., Sousa, P., Rocha, M., and Rio, M.
(2011). Symbiotic filtering for spam email detection.
Expert Systems with Applications, 38(8):9365–9372.
Lopez-Herrera, A., Herrera-Viedma, E., and Herrera, F.
(2008). A multiobjective evolutionary algorithm for
spam e-mail filtering. In Intelligent System and
Knowledge Engineering, 2008. ISKE 2008. 3rd Inter-
national Conference on, volume 1, pages 366 –371.
M
´
endez, J., Cid, I., Glez-Pe
˜
na, D., Rocha, M., and Fdez-
Riverola, F. (2008). A Comparative Impact Study of
Attribute Selection Techniques on Naive Bayes Spam
Filters. In Springer, editor, 8th Industrial Conference
on Data Mining, volume LNAI 5077, pages 213–227.
Metsis, V., Androutsopoulos, I., and Paliouras, G. (2006).
Spam filtering with naive bayes – which naive bayes?
In Third Conference on Email and AntiSpam CEAS,
pages 125–134. Citeseer.
Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., and
Euler, T. (2006). Yale: Rapid prototyping for complex
data mining tasks. In Proc. of the 12th ACM SIGKDD
international conference on Knowledge discovery and
data mining, pages 935–940. ACM.
Radcliffe, N. (1993). Genetic set recombination. Founda-
tions of Genetic Algorithms, 2:203–219.
Zhang, Y., Li, H., Niranjan, M., and Rockett, P. (2008). Ap-
plying cost-sensitive multiobjective genetic program-
ming to feature extraction for spam e-mail filtering.
In Proc. of the 11th European conference on Genetic
programming, pages 325–336. Springer-Verlag.
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