ATTRIBUTE CONSTRUCTION FOR E-MAIL FOLDERING BY
USING WRAPPERED FORWARD GREEDY SEARCH
Pablo Bermejo, Jos
´
e A. G
´
amez and Jos
´
e M. Puerta
Computing Systems Department. Intelligent Systems and Data Mining research group (SIMD)
University of Castilla-La Mancha, Albacete, Spain
Keywords:
Text mining, e-mail foldering, attribute construction, X-of-N attribute, wrapper approach, forward search.
Abstract:
E-mail classification is one of the outstanding tasks in text mining, however most of the efforts in this topic
have been devoted to the detection of spam or junk e-mail, that is, a classification problem with only two
possible classes: spam and not-spam. In this paper we deal with a different e-mail classification problem
known as e-mail foldering which consists on the classification of incoming mail into the different folders
previously created by the user. This task has received less attention and is quite complex due to the (usually
large) cardinality of the class variable (the number of folders). In this paper we try to improve the classification
accuracy by looking for new attributes derived from the existing ones by using a data-driven approach. The
attribute is constructed by taking into account the type of classifier to be used later and following a wrapper
approach guided by a forward greedy search. The experiments carried out show that in all the cases the
accuracy of the classifier is improved when the new attribute is added to the original ones.
1 INTRODUCTION
One of the most common tasks in text-mining is clas-
sification (Lewis, 1992), where the goal is to decide
which class a given document belongs to among a
set of classes. Apart of the needed preprocessing in
any data mining task, in this case we need to perform
some extra preprocessing in order to transform the un-
structured text document into a data structure suscep-
tible of being used as input by a classifier learning
algorithm. Concretely, we seek for a bi-dimensional
table with rows representing documents (e-mails) and
columns representing predictive attributes or terms
1
.
The selection of a good set of terms to describe the
documents is of great importance in order to achieve
a good classification rate (Bekkerman et al., 2005).
The quality of the selected subset of terms depends
on the importance of each attribute respect to its class,
dependences among the included terms and existence
of negative attributes (those which reduce the correct
classification rate). Once the initial set of terms has
1
In text mining applications, most of the attributes are
words or tokens appearing in the documents, and in general
they are referred to as terms
been obtained, commonly based upon information re-
trieval techniques (Salton and Buckley, 1987), we can
try to improve the resulting subset in two supervised
(i.e., class dependent) ways:
• Attribute selection. This is a task (Liu et al., 2002)
w
idely studied in data mining, consisting basi-
cally on reducing the set of available features (at-
tributes) through the selection of the most impor-
tant ones.
• Attribute construction. Sometimes it is possible to
obtain higher quality attributes from those avail-
able (e.g. area from length and width). This task is
called attribute construction (Larsen et al., 2002).
In this work we deal with the second option,
specifically applied to a particular task inside text
classification: classification of e-mail into folders
(Bekkerman et al., 2005; Klimt and Yang, 2004). E-
mail classification has been widely studied applying
it to classification or filtering of junk mail (or spam),
but its application to (semi)automatic classification of
mail into folders (e-mail foldering) defined by user
has not been so deeply investigated (Bekkerman et al.,
2005).
247
Bermejo P., A. Gámez J. and M. Puerta J. (2007).
ATTRIBUTE CONSTRUCTION FOR E-MAIL FOLDERING BY USING WRAPPERED FORWARD GREEDY SEARCH.
In Proceedings of the Ninth International Conference on Enterprise Information Systems - AIDSS, pages 247-252
DOI: 10.5220/0002376902470252
Copyright
c
 SciTePress

In this paper we propose to use attribute construc-
tion in order to improve the quality of the classifiers
learnt from them. In this sense the goal of the work is
to construct a single attribute that collects possible de-
pendences among those used as building blocks, and
whose use improves the accuracy of the classification
process. Thus, our main contribution is the design of
such a new derived attribute and of the search algo-
rithms used to look for it.
The work is structured in the following sections:
Section 2 details the model used to classify e-mails
into folders; next in Section 3 we introduce the con-
cept of attribute construction and the type of attribute
we construct; in Section 4 we describe the search al-
gorithms proposed to look for good derived attributes;
finally, in the last two sections we present the experi-
ments carried out and our analysis and conclusions.
2 TEXT (E-MAIL)
CLASSIFICATION
The main differences between standard classification
and classification of text are: the need of preprocess-
ing the unstructured documents in order to get a stan-
dard data mining dataset (bi-dimensional table); and
the usually large number of features or attributes in
the dataset. In this work we focus on models of bag-
of-words. In these models, a document text is re-
garded as a set of words or terms without any kind
of structure. Therefore, these models are based upon
the fact that a document can be represented with a
selection of terms from a dictionary or vocabulary
V. The identification process of the set of terms (V)
can be performed in two stages: (1) deleting irrele-
vant attributes or stop-words; and (2) selection of a
set of most relevant terms for the classification task.
From this point our dataset can be observed as a bi-
dimensional matrix M[
numDocs, numTerms], where
M
ij
is a real number representing (some transforma-
tion of) the frequency of appearance of term j in doc-
ument i.
Our interest in this work focuses on automatic
classification of mails, regarding them as a set of doc-
uments and without considering any special feature.
Formally the problem can be established from a set of
mailsC
train
= {(d
1
, l
1
), . . ., (d
|D|
, l
n
)}, such that d
i
∈ D
is the document which corresponds to the ith mail of
the set of documents or mails D, l
j
corresponds to
the folder that contains it and L = {l
1
, . . ., l
|L|
} is the
set of possible folders. The goal is to build a clas-
sifier c : D → L. In this work we will focus on the
probabilistic classifier model Nave Bayes Multino-
mial (NBM) to solve the mail classification problem.
In (McCallum and Nigam, 1998), NBM and NB Bi-
nomial are compared resulting in NBM to achieve a
better performance when vocabulary size is not small.
Our main goal is the search of new features from the
available terms to improve the correct classification
of new mails and, therefore, our effort is performed
previously (or simultaneously) to the application of
NBM, and in general, this task is valid independently
of the classifier used.
2.1 Nave Bayes Multinomial
Formally a NBM classifier assumes independence
among terms once the class they belong to is known.
Besides, this model lets us regard not only those terms
appearing in each document but also the frequency of
appearance of each term. This is important, because
we can presume that a high appearance frequency in-
creases the probability of belonging to a particular
class. In the model here considered, the class a doc-
ument belongs to is decided by calculating the class
which maximises the Bayes rule (eq. 1), computing
the conditional probability as shown in equation 2:
P(c
j
|d
i
) =
P(c
j
)P(d
i
|c
j
)
P(d
i
)
(1)
P(d
i
|c
j
) = P(|d
i
|)|d
i
|!
∏
|V|
t=1
P(w
t
|c
j
)
N
it
N
it
!
(2)
N
it
being the amount of times that term w
t
appears in
document d
i
, |V| the size of our vocabulary, |D| the
amount of documents to classify and |d
i
| the length
of document i. Equation 2 assumes independence
among terms. The independence assumption among
parameters is a problem itself since it is not realistic
in real databases. Besides, this assumption gets even
more troublesome when using the multinomial model
(Lewis, 1998) because it assumes not just indepen-
dency among different terms but also among various
occurrences of the same term; this is alleviated by the
type of new attribute that we propose in this work (see
Section 3.1).
3 ATTRIBUTE CONSTRUCTION
As it has been commented above, the quality of the
available attributes is decisive to reach a good accu-
racy classification rate. From this idea, several tech-
niques (Hu, 1998a) have been developed for the con-
struction of new attributes through the application of
some operations over the available ones. Attribute
construction is most important when working with
real databases, since they have not been created think-
ing about their application to data mining tasks and
ICEIS 2007 - International Conference on Enterprise Information Systems
248

thus it is possible they do not contain attributes mean-
ingful enough for a beneficial use (Freitas, 2001).
In attribute construction the main goal is to get
a new attribute which represents the regularities of
our database in a simpler way to detect them and
thus making the classification task easier (Otero et al.,
2003). In this paper we aim to design a single new
constructed attribute highly optimised for the classi-
fication process and inspired in the X-of-N method-
ology by using algorithms that according to the tax-
onomies about attribute construction described in
(Hu, 1998a) and (Hu, 1998b) fall in the categories
of combination and data driven, because we seek to
identify dependences between the original attributes
by interacting with the classifier (NBM) to be used
later.
3.1 Attribute X-of-N
A
X-of-N attribute (Zheng, 1995) can be viewed as a
logical formula, concretely a disjunction of clauses
(attribute-value pairs) and its value for a given in-
stance is calculated by counting the number of true
clauses when using that instance as input for the
X-of-
N
formula. In a X-of-N attribute N refers to the number
of (different) attributes included in it. Of course, the
attribute takes values in the set {0,. . . , N}.
For instance, if we have a dataset with 5 predic-
tive attributes (A,B,C,D,E) and we make N = 3, then
X − of − {A = a
1
, C >= 17.2, E = true} could be a
derived attribute from A, C and E. Now, if we have
the two following instances
id A B C D E
r1 a
1
0 19.45 34 false
r2 a
2
0 15.3 26 true
then it is easy to see that X − of − {A = a
1
, C >=
17.2, E = true}(r1) = 2 and X −of −{A= a
1
, C >=
17.2, E = true}(r2) = 1.
As we can observe from the example, compari-
son operators ({=, ≤, ≥, >, <, etc.}) are usually con-
sidered to build the clauses or attribute-value pairs.
In our problem, all the attributes are continu-
ous representing frequencies of appearance, thus each
attribute-value pair could be written as A
i
≤ t, t being
a threshold. The main disadvantage of this approach
is the complexity of the searching process because
we have to identify which attributes are included in
the constructed one and also the specific threshold for
each attribute, leading to a very large search space.
An alternative to this representation is to restrict our
attribute-value pairs to take only two ways: A
i
> 0 or
A
i
= 0. This representation leads to a simpler search
space and also allows us to explicitly represent inclu-
sion and exclusions of terms in our constructed at-
tribute. On the other hand, this representation cor-
responds to the boolean model in which we lose the
information about frequencies. Finally, and trying to
seek for a compromise between the search process
and the expressiveness of the constructed attribute,
we propose to consider only
2
A
i
> 0 attribute-value
pairs but modifying the evaluation process of the con-
structed
X-of-N attribute in order to take into account
the frequencies. Thus, if M
i
represents an instance (a
row in our dataset), we compute:
X −of − {A
j
1
> 0, . . . , A
j
r
> 0}(M
i
) =
r
∑
k=1
M
ij
k
.
In this way we take into account the frequencies
which is really interesting having in mind the classifi-
cation model (NBM) to be used and, simultaneously
we simplify the search process to the point of simply
looking for the attributes to be included in the con-
structed attribute. This idea, as far as we know, has
not been proposed in the literature.
4 X-OF-N SEARCH ALGORITHM
In the previous sections we have identified relevant
aspects for the design of the search algorithm:
• We look for a single constructed attribute.
• If {A
1
, . . ., A
n
} is the set of available attributes,
them the search space is {0, 1}
n
, constrained to
the fact that the number of 1’s in a potential solu-
tion must be at most N.
• We use a wrapper approach based on the NBM
classifier. That is, each time a potential solution
has to be evaluated, a classifier has to be trained
and evaluated.
Because of these conditions and because this is
a preliminary approach we consider it appropriate
to use a greedy algorithm to deal with our prob-
lem. Greedy search is a kind of metaheuristics in-
side a global metaheuristics set known as Construc-
tive Metaheuristics. Greedy search is based upon con-
structing the solution step by step, choosing each time
the best member of the solution and if needed we can
stop at any time returning the current solution. On
the other hand this behaviour leads to find just a lo-
cal optimal because components have been chosen in
a local manner without any perspective of the whole
search space, however its performance tends to be
quite high because it needs lower computation times
(than stochastic methods) and besides the solutions it
returns might be close to the global optimum.
2
Initially we considered also A
i
= 0 attribute-value pairs
but we discard them because their use lead to a lower per-
formance
ATTRIBUTE CONSTRUCTION FOR E-MAIL FOLDERING BY USING WRAPPERED FORWARD GREEDY
SEARCH
249

4.1 Forward Search
Forward search is probably one of the most used
techniques in variable selection, and due to the way
in which we have designed our
X-of-N algorithm, it
seems appropriate to use this type of greedy search.
Our algorithm will test at each step all the available
attributes in our dataset to add a new condition to the
X-of-N attribute, then it will choose the attribute which
increases the correct classification rate the most. The
search will stop when no improvement is found. Al-
gorithm 1 shows the pseudocode of forward search.
In A (attributes; {A
1
, . . . , A
n
}); M (the dataset)
Out The attribute X-of-N constructed
1 B ← A; X-of-N ←
/
0;
2 bestacc ← NBM(A,M); go ← true
3 While (|B| > 0 and go) do {
4 go ← false; best ← null
5 C ← B
6 for(i=1; i < |C|; i++) {
7 acc ← NBM(A∪ (X-of-N+ {C
i
}), M)
8 if (acc > bestacc)
9 best ← i; bestacc ← acc
10 }
11 if (best 6= null) {
12 X-of-N += {C
best
}
13 B -= {best}; go ← true
14 }
15 }
16 return X-of-N
Algorithm 1: X-of-N forward search.
At each iteration of the algorithm, the for loop
runs over all possible candidates (C) in order to
choose the best one to be included in the X-of-N at-
tribute. In algorithm 1 the set of candidates is equals
to all the available attributes not yet included in the
target attribute
X-of-N. This means to learn and evalu-
ate n classifiers at the first iteration, n−1 at the second
iteration, etc., which can be prohibitive if n is large,
as it is usually the case. Because of this we introduce
two different modifications:
• Let {X
k
1
, . . ., X
k
r
} be the set of attributes already in-
cluded in the target attribute
X-of-N, then instead of
search over all the remaining attributes, we only look
for attributes that appear with any of the already in-
cluded attributes in some documents. This heuristic
seems to be reasonable because we are trying to catch
dependences between the variables included in the
X-
of-N
attribute. Thus, we replace step 5 in Algorithm 1
by:
C ← {B
j
| ∃
i,l=1..r
s.t. M[d
i
, B
j
] · M[d
i
, X
k
l
] > 0 }
where M[d
i
, B
j
] refers to the frequency of attribute B
j
in document d
i
.
• Our second simplification or heuristics is even more
greedy, because we limit the candidates to those terms
who share any document with the last attribute (X
l
)
entered in
X-of-N, thus:
C ← {B
j
| ∃
i
s.t. M[d
i
, B
j
] · M[d
i
, X
l
] > 0 }.
In this way we expect to make our algorithm faster
but without decreasing significantly the accuracy.
5 EXPERIMENTS
5.1 Test Suite
The database used in our experiments is the Enron
Corpus, the same used in (Bekkerman et al., 2005;
Klimt and Yang, 2004). Mail from 7 users of this
corpus and a temporal line in increasing order can be
downloaded from http://www.cs.umass.edu/∼ronb.
As far as we know, the most in deep study car-
ried out on this corpus can be found in (Bekkerman
et al., 2005), where mails of seven users were selected
for the classification study. We have carried out the
same preprocessing process that in (Bekkerman et al.,
2005); that is, stop-words deletion, removal of folders
with 2 or less mails and we have also flattened folder
levels to just one level. To do this preprocessing we
have coded our own program that reads directories
of text files and, by using the Lucene information re-
trieval API (http://lucene.apache.org/who.html), pro-
cesses them and produces as output an arff file
3
, that
is, a file following the input format for the WEKA
data mining workbench (Witten and Frank, 2005).
We have implemented the algorithms using LiO
(Mateo and de la Ossa, 2006), a library of metaheuris-
tics developed in Java by our research group which
lets the programmer use or extend a great amount
of metaheuristics. The wrapper approach is carried
out by calling the NBM included in the WEKA API,
but using the time-based split evaluation proposed in
(Bekkerman et al., 2005). This validation method
consists on ordering mails based upon its timestamp
field, and then training with the first t mails and test-
ing using next t. After that, training is performed with
first 2t mails and testing with next t, and that way until
it is finally trained with the first (K − 1)t mails and it
its tested with the remaining ones. Being K the num-
ber of time splits the total amount of mails is divided
into, and t the number or mails in each time split; in
our case we have set t=100.
5.2 Results
We have selected the same users as in (Bekkerman
et al., 2005), whose corresponding datasets are de-
3
Using a sparse representation
ICEIS 2007 - International Conference on Enterprise Information Systems
250

scribed in Table 1. As we can see there is a great vari-
ability both in the number of instances and classes,
so we can expect to have really different classifica-
tion rates for them
4
. The results of NBM over these
datasets are shown in column 1 (original) of Table 2
when about 14000 attributes were used as input. We
also check the performance of NBM when some easy
attribute selection is carried out, concretely we build
14 new datasets by projecting the original 7 datasets
over the 100 and 1000 first attributes ranked by us-
ing mutual information. As we can see in Table 2
with this type of attribute selection the accuracy of
the classifier decreases (on average) 2.5% to the orig-
inal dataset when using the first 100 attributes, while
it increases (on average) 3.3% when using the 1000
first attributes. In the following we experiment with
100 and 1000 attributes projection in order to analyse
the impact of adding the
X-of-N attribute to them.
Table 1: Instances and Classes for each user.
Instances Classes
beck-s 1971 101
farmer-d 3672 25
kaminski-v 447 41
kitchen-l 4015 47
lokay-m 2489 11
sanders-r 1188 30
williams-w3 2769 18
Table 2: Classification rates for each user.
Original 100 At. 1000 At.
beck-s 33,945 26,131 31,546
farmer-d 65,893 69,742 71,045
kaminski-v 39,593 35,889 43,559
kitchen-l 36,618 32,200 32,759
lokay-m 72,099 62,974 74,181
sanders-r 46,956 49,051 62,698
williams-w3 77,915 80,732 80,438
Tables 3 and 5 show the results of using the X-of-N
attribute during the classification process. The tables
show the accuracy improvement (%) when the new
attribute is used as well as the number of attributes
(i.e. the value of N) included in the constructed at-
tribute and the columns represent the algorithms we
are going to test. Let us to analyse these results:
• With respect to the improvement achieved we can
observe it shows a great variability over the differ-
ent users, but on the average the accuracy improves
1.5% in the 100-attributes case and 1% in the 1000-
attributes case. Although these figures are not very
impressive from an absolute point of view, we have to
4
In fact, as pointed in (Klimt and Yang, 2004; Brutlag
and Meek, 2000) performance among different users varies
much more than variation between different classifiers
Table 3: Improvement (and value of N) for each type of
candidate set when using 100 attributes as input.
Last X-of-N All
attrib attribs. attribs.
beck-s 2,24 (15) 2,31 (13) 2,31 (13)
farmer-d 0,71 (8) 0,71 (8) 0,71 (8)
kaminski-v 1,04 (8) 1,02 (8) 1,02 (9)
kitchen-l 1,33 (8) 1,37 (9) 1,37 (9)
lokay-m 1,19 (7) 1,19 (7) 1,19 (7)
sanders-r 3,31 (9) 3,31 (9) 3,31 (9)
williams-w3 1,03 (4) 1,03 (4) 1,03 (4)
take into account our starting point, that is, the (usu-
ally) low percentage of success when using the origi-
nal set of attributes.
• With respect to the three versions of our greedy
search we can see that accuracies are similar in the
100-attributes data set, being a possible explanation
that these 100 attributes are so important and so
they appear in most of the documents. In the 1000-
attributes case the extremely greedy behaviour of us-
ing as candidate only those attributes sharing at least
a document with the last attribute added to
X-of-N
performs slightly worse than the other two versions.
However, from the results it seems reasonable to use
as candidate only those attributes sharing docs with
the current
X-of-N because the result is almost the
same as when using all the attributes as candidates,
being the amount of CPU time clearly lower in this
case as shown in Table 4 for the 1000-attributes case.
• With respect to the number of attributes included in
the X-of-N attribute, it varies depending on the case
(user) and also on the type of candidate list used dur-
ing the forward search. Thus, in the 100-attributes
case the figures are quite similar for the three meth-
ods, and N is relatively small what yields to inter-
pretable attributes that usually include the name or
surname of the sender, the e-mail address, etc. More
variability is observed in the 1000-attributes case,
both among users and methods. In this case the
simpler version adds few attributes while the other
two versions add significantly more variables, lead-
ing to less interpretable results. We think that if inter-
pretability is a goal, then an upper bound (i.e. N=7 as
used by other authors) should be fixed, because in this
way we get simpler
X-of-N attributes but maintaining
similar rates of improvement in accuracy.
6 CONCLUSION
In this paper we have proposed to look for a single
X-of-N attribute in e-mail foldering. The attribute has
been designed according to the target task, and search
ATTRIBUTE CONSTRUCTION FOR E-MAIL FOLDERING BY USING WRAPPERED FORWARD GREEDY
SEARCH
251

Table 4: Greedy execution time (hours).
Last attrib. All attrib. No candid.
beck-s 2,14054 9,98000 18,80485
farmer-d 5,988998 16,00856 21,52744
kaminski-v 7,60864 27,47413 36,43047
kitchen-l 24,11993 60,77219 66,94359
lokay-m 4,60200 4,46918 10,06760
sanders-r 1,24783 1,70260 2,07056
williams-w3 6,20118 10,23979 11,26965
Table 5: Improvement (and value of N) for each type of
candidate set when using 1000 attributes as input.
Last X-of-N All
attrib attribs. attribs.
beck-s 1,56 (9) 2,01 (18) 2,03 (20)
farmer-d 0,40 (5) 0,51 (10) 0,47 (12)
kaminski-v 1,15 (11) 1,36 (25) 1,37 (26)
kitchen-l 0,59 (7) 0,76 (18) 0,80 (21)
lokay-m 0,47 (5) 0,51 (5) 0,52 (10)
sanders-r 1,08 (4) 1,08 (4) 1,08 (4)
williams-w3 0,58 (3) 0,73 (6) 0,73 (6)
methods to look for it have also been designed and
tested. The experiments carried out show that the
use of the new attribute is beneficial with respect to
the classifier accuracy, and also that in many cases
it is interpretable. Besides, its construction process
is not classifier-specific. With respect to the search
methods we can say that the one considering as can-
didates to be included in
X-of-N only those attributes
sharing docs with the current
X-of-N, exhibits the best
tradeoff between CPU requirements and accuracy im-
provement. For the future we plan to go deeper in
this study (different designs for
X-of-N and different
search methods) and also to consider the inclusion of
more than one X-of-N attributes or the combination
of attribute selection and construction instead of per-
forming them in a two-stage process as in the current
work.
ACKNOWLEDGEMENTS
This work has been supported by the JCCM under project
PBI-05-022, MEC under project TIN1504-06204-C03-03
and the FEDER funds.
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