Using Class Frequency for Improving Centroid-based Text Classification

ACEEE Int. J. on Information Technology, Vol. 02, No. 02, April 2012

Using Class Frequency for Improving Centroid-based
Text Classification
Verayuth Lertnattee1 and Chanisara Leuviphan1
1
Department of Health-related Informatics, Faculty of Pharmacy
Silpakorn University, Maung, Nakorn Pathom, 73000 Thailand
E-mail: verayuths@hotmail.com, verayuth@su.ac.th, chanisara@su.ac.th

Abstract— M ost previous works on text classification, This paper investigates the usefulness of inverse class
represented importance of terms by term occurrence frequency frequency in a more systematic way. The traditional term
(tf) and inverse document frequency (idf). This paper presents frequency and inverse document frequency utilize information
the ways to apply class frequency in centroid-based text within classes and in the whole collection of training data.
categorization. Three approaches are taken into account. The
Besides these two sources of information, information among
first one is to explore the effectiveness of inverse class
frequency on the popular term weighting, i.e., TFIDF, as a classes so called inter-class information is expected to be
replacement of idf and an addition to TFIDF. The second useful. An inverse class frequency can utilize this source of
approach is to evaluate some functions, which are used to information. The effectiveness of applying class frequency
adjust the power of inverse class frequency. The other approach in term weighting and classification process is investigated.
is to apply terms, which are found in only one class or few Three approaches are taken into account. The first one is to
classes, to improve classification performance, using two-step explore the effectiveness of inverse class frequency on the
classification. From the results, class frequency expresses its
usefulness on text classification, especially the two-step popular term weighting, i.e., tf  idf , as a replacement of
classification.
idf and an addition to tf  idf . The second approach is
Index Terms—text classification, term weighting, class to evaluate some functions, which are used to adjust the
frequency, linear classifier power of inverse class frequency. The other approach is to
apply terms that are found in only one class or few classes,
I. INTRODUCTION to improve classification performance, using two steps of
classification. In the rest of this paper, section II presents
The increasing availability of online text information, there
centroid-based text categorization. Term weighting in
has been extreme need to find and organize relevant
centroid-based text classification by frequency-based
information in text documents. The automated text
patterns is given in section III. Section IV described three
categorization (also known as text classification) becomes a
approaches for applying class frequency in text classification.
significant tool to organize text documents efficiently. A
The data sets and experimental settings are described in
variety of classification methods were developed and used
section V. In section VI, a number of experimental results are
in different schemes, such as probabilistic models [1], neural
given. A conclusion is made in section VII.
network [2], example-based models (e.g., k -nearest neighbor)
[3], linear models [4], [5], support vector machine [6] and so II. CENTROID-BASED TEXT CLASSIFICATION
on. Among these methods, a linear model called a centroid-
based method is attractive since it has relatively less In the centroid-based text categorization, a document (or
computation than other methods in both the learning and a class) is represented by a vector using a vector space model
classification stages. Despite less computation time, a with a bag of words (BOW) [9]. The simplest and popular
centroid-based method was shown in several literatures one is applied term frequency (tf) and inverse document
including those in [4], [5], to achieve relatively high accuracy. frequency (idf) in the form of tf  idf as a term weight for
In this method, an individual class is modeled by weighting representing a document. In a vector space model, given a
terms appearing in training documents assigned to the class. set of documents D = {d1 , d 2 ,..., d|D| } , a document d j is
This makes classification performance strongly depend on
represented by a document vector
term weighting applied in the model. Most previous works of 
a centroid-based method focused on weighting factors related d j ={w1 j , w2 j ,...,w|T| j }={tf1 j idf , tf2 j idf2 ,...,tf|T| j idf|T|} ,
1
to frequency patterns of terms or documents in the class.
The most popular two factors are term frequency (tf ) and where wij is a weight assigned to a term ti in a set of terms

inverse document frequency (idf ) . Some previous works, (T ) of the document. In this definition, tf ij is term frequency
such as those in [7], [8], attempted to apply another factor
of a term ti in a document d j and idf i is inverse document
called inverse class frequency (icf ) . However, the impact
of this factor needs more investigation. frequency, defined as log (| D | /df i ) . Here, | D | is the total
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number of documents in a collection and df i is the number This represents an average class term frequency in a class.
The tf is considered as an intra-class factor. A term that is
of documents, which contain the term ti . Besides term
weighting, normalization is another important factor to important for a specific class, should have a high tf . The
 ik
represent a document or a class. Class prototype ck is
tf deals with the first property of the significant terms for
obtained by summing up all document vectors in C k and classification. Term frequency alone may not be enough to
then normalizing the result by its size. The formal description represent the contribution of a term in a document. To achieve
   a better performance, the well-known inverse document
of a class prototype ck is djC d j / || djC d j || , where frequency can be applied to eliminate the impact of frequent
k k
terms that exist in almost all documents. The idf i is the
C k = { d j | d j is a document belonging to the class ck }.
inverse ratio of the number of training documents that contain
The simple term weighting is tf  idf where tf is an average the term ti to the total number of training documents. It is
class term frequency of the term. The formal description of usually applied in the form of the logarithm value of its original
value. It deals with the second property of the significant
tf is  djC k
tf ijk / | Ck | , where | C | is the number of
k terms. However, this tends to be true if those documents,
which hold the terms, are in the same class. Inversely, if the
documents in a class ck . Term weighting described above
distribution of the terms is uniform in all classes, idf
can also be applied to a query or a test document. In general,
becomes useless and is not helpful in classification. The idf
the term weighting for a query is tf  idf . Once a class
prototype vector and a query vector have been constructed, is considered as a collection factor, i.e., its value is the same
the similarity between these two vectors can be calculated. for a particular term, independent of arrangement of classes
The most popular one is cosine distance [10]. This similarity in a collection.
can be calculated by the dot product between these two The first and second items can be coped by the
vectors. Therefore, the test document will be assigned to the conventional term frequency and inverse document
class whose class prototype vector is the most similar to the frequency, respectively. However, for the third property, it is
vector of the test document. necessary to utilize other frequency factors. For this property,
class frequency is expected to be useful. Class frequency
III. TERM WEIGHTING IN CENTROID-BASED T EXT enables the classifier to utilize the information among classes.
CLASSIFICATION BY FREQUENCY-BASED PATTERNS It is considered as an inter-class factor. Some collections
may have several possible organizations of documents
This section presents concept of term weighting in (viewpoints). For example, a collection may be grouped into
centroid-based text classification using frequency-based a set of classes based on its content (e.g., course, faculty,
patterns. Before the detail is described, some general project, student, ...) or it may be grouped by its university
characteristics of terms that are significant for representing a (e.g., Cornell, Texas, Washington, Wisconsin,...). In these two
certain class, are listed below:
cases, the collection factor (e.g., idf ) of a term is identical
• A term tends to be a representative of a certain class, it
should appear frequently with high occurrence frequency while the inter-class factor of that term is varied. An important
within the class. term of the specific category seems to exist in one class or
• An important term seems to exist in relatively a few only few classes (the third property of significant terms).
documents while a general term tends to appear in most Therefore, we can apply class frequency to represent
documents in a collection. important of that term. In the past, there were some works
• A crucial term seems to exist in only one class or few utilizing class frequency in general forms, such as logarithmic
classes. of inverse class frequency ( icf ). It is an analogous of idf .
To realize these characteristics, frequency-based patterns in
a training collection can be applied. Three main frequency In some situations that we cannot calculate idf such as in
factors are term frequency, document frequency and class [8], it is possible to set a sentence as a processing unit instead
frequency. The simplest and popular one is applied term of a document, and hence icf replaces idf . Although the
frequency (tf ) and inverse document frequency (idf ) in
icf seems to be useful, it is not popular for applying into
the form of tf  idf for representing a document. In a term weighting.
centroid-based method, term frequency of a class vector
comes from average value of term frequencies of documents IV. APPLYING CLASS FREQUENCY IN TEXT CLASSIFICATION

in a class. Due to this, we use a symbol tf instead of tf . In this paper, the usefulness of class frequency is
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presented on both the concept and experiments. Three contrary, it demotes tf and tf  idf when its value less
approaches are taken into account: (1) the effectiveness of
than 1.
icf in term weighting (2) several functions are applied to
B. Functions to modify ICF.
icf to enhance classification performance and (3) the algo-
rithm to apply terms which are found in only one class or few In this approach, several functions are applied to icf for
classes, to improve classification performance, using two adjusting level of promoting and demoting to tf and
steps of classification. The detail is decribed as follow:
A. The Effectiveness of ICF in Term Weighting tf  idf . The first pattern is adjusting the power of icf .
The popular method to utilize class frequency is in the
When the term weighting is tf  idf  icf , a new term
form of logarithmic function of inverse class frequency. The
equation is shown below. weighting is shown below

|C | tf  idf  icf  (2)
ICFi = log (1)
cf i Here, the  is the power of icf . The icf promotes the
Here cf i is the number of classes that contain a term ti . The tf  idf when its value > 1. If the power of the icf is greater
effect of the icf is to promote a term which occurs in only than 1, it is more powerful to promote or demote a term than
few classes (later called few-class terms) and demote a term a normal icf . On the other hand, when the power of the icf
which appears in many classes (later called most-class terms). is less than 1, it is less powerful to promote or demote a term
In the extreme case, where a term occurs only in one class than a normal icf . The optimum value of the  depends on
(cf i = 1) , the function obtains the maximum value. These data sets. In the first pattern, a term may be promoted or
terms are called one-class terms. We belief that few-class demoted. The second pattern considers only promotion of a
terms are quite important for classifying documents. Inverse term. An example of a term weighting formula is shown below
class frequency promotes the importance of these terms.
tf  idf  (icf  1) (3)
Inversely, when a term occurs in all classes (cf i =| C |) , a From the equation, terms are found on all classes, are given a
function of inverse class frequency achieves a minimum value,
term weight of the tf  idf . The other terms are given with
i.e., 0. These terms are called all-class terms. They are
considered as less important and may cause the classifier the values which are higher than the tf  idf .
misclassify the document. However, their usefulness also
depends on their term frequencies. The promotion of few- C. Two-step Classification
class terms is more effective than the demotion of most-class
In this approach of classification, class frequency of a
terms since it may demote the important terms that occur in
term is applied to select representations of the class prototype
all classes but useful for classifying documents. The icf and the test document vectors. The representative vectors
can be included into term weighting. Two simple patterns are are based on n-class terms. A representative vector of n-
class terms, means it represents by terms which occur in
applied. The first pattern is tf  icf , i.e., substitution of the
1,2,...,n classes where 1  n | C | . In the first step of
idf with icf . The other pattern is tf  idf  icf , i.e., classification, the n is set. A test document is classified to an
appropriated class, based on n-class term. The rest of the
include the icf in the tf  idf . In normal situation, the test documents, which have not n-class terms as their
number of documents is greater than the number of classes representation, will be classified using all terms in the second
step. For example, when n=1, i.e., the representation vectors
( | D |>| C | ). This means the idf promotes the importance of prototypes and test documents is only a set of terms that
of terms more than the icf . When the logarithm of base 2 is are found only in one class. A test document whose have
one-class terms of a class, is automatically classified to that
used, the value of icf for a term is equal to 1 when the term class in the first step. In the second step, the rest of the test
is found in the half number of the total classes in a data set documents will be classified by all terms.
( cf i =| C | /2 ). Therefore, the value of icf > 1 when
V. EXPERIMENTAL SETTING
cf i <| C | /2 and icf < 1 when cf i >| C | /2 . If the value To evaluate our concept about class frequency, three
collections called WebKB, 7Sectors and 20Newsgroups are
of icf more than 1, it promotes tf and tf  idf . On the used. The WebKB, is a collection of web pages of computer
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science departments in four universities with some additional WebKB1, WebKB2, 7Sectors and 20Newsgroups. Table I
pages from other universities. This collection can be viewed showed the result in forms of the classification accuracy.
as two-dimensional viewpoints. In our experiment, we use TABLE I. EFFECT OF ICF ON THE FOUR D ATA SETS
the four most popular classes: student, faculty, course and
project. This includes 4,199 web pages. Focusing on each
class, five subclasses (the 2 nd dimension) are defined
according to the university a web page belongs to: Cornell,
Texas, Washington, Wisconsin and miscellaneous. We use
this collection in two viewpoints: first dimension (WebKB1) From the result, some observations can be made as follows.
and second dimension (WebKB2). The total number of data
sets for this collection is two. Therefore, the effect of inverse The performance of a prototype vector with tf  idf , is
class frequency with different numbers of classes on the
better than that of a prototype vector with tf  icf on three
same collection, can be evaluated. The second collection is
the 7Sectors, a data set from CMU World Wide Knowledge data sets, except only the 7Sectors. The performance of
Base Project from the CMU Text Learning Group. This data classifiers with tf  icf on the same collection but different
set has seven classes: basic material, energy, financial,
healthcare, technology, transportation and utilities. The viewpoints, i.e. WebKB1 and WebKB2, may be different with
number of documents in this collection is 4,582. The third a large gap. The most effective term weighting for all data
collection is 20Newsgroups (20News). The articles are sets is tf  idf  icf . It can be concluded that the icf
grouped into 20 different UseNet discussion groups. It
contains 19,997 documents and some groups are very similar. express its usefulness when it is used to combine with
All experiments were performed on closed test, .i.e., we tf  idf . In generally, the should not been used to replace
used a training set as a test set. The performance was mea-
the .
sured by classification accuracy defined as the ratio between
the number of documents assigned with correct classes and B. Funtions to Modify ICF
the total number of test documents. As a preprocessing, some In this experiment, three functions are applied to , i.e., ,
stop words (e.g., a, an, the) are excluded from all data sets. and . These denote by (ICF+1), SqrtICF and (ICF)^2,
For the HTML-based data sets, all HTML tags (e.g., < B > , respectively. The result is shown in Table II. Note that the
< /HTML > ) were omitted from documents to eliminate the result of TFIDFICF is represented again for comparison.
affect of these common words and typographic words. All Some observations on several functions on ICF can be made
headers are omitted from Newsgroups documents. A unigram as follows. The (ICF)^2 is the best by average on the four
model is applied in all experiments. data sets. It can improve the performance of the classifier on
Three experiments are performed. The first experiment is three of four data sets, with the exception of WebKB2. On
to investigate effects of inverse class frequency on the four the 20News, the (ICF)^2 improve the performance with a gap
data sets of the three collections. The icf is multiplied to of 4.43%. Although the performance of the classifier is the
best by (ICF+1) on WebKB2, the average performance is less
tf and tf  idf . The standard term weighting, tf  idf , is than the ICF. The average performance of the classifier by
used as a baseline for comparison. The default term weighting the SqrtICF is a little bit less than ICF.
for a test document is tf  idf on all experiments. In the C. Two-step Classification
second one, we investigate effects of the three functions on In the last experiment, two-step classification is used. We
apply n-class terms in the first step. The values of n are 1 and
icf . In the last experiment, two-step classification is
2, i.e., one-class terms and two-class terms are investigated.
performed by using one-class terms and two-class terms in For n=1, only a set of one-class terms is used as a
the first step. representation of prototype and test document vectors. A
test document that contains a set of one-class terms can be
VI. EXPERIMENTAL RESULTS automatically classified to a class of those terms. In case of
A. Effect of ICF n=2, a set of one-class terms and two-class terms is used as
a representation.
In this first experiment, the effect of inverse class fre-
TABLE II. EFFECT OF D IFFERENT FUNCTIONS APPLIED TO ICF
quency are investigated on tf and tf  idf . Three differ-

ent term weightings, i.e., tf  idf (TFIDF), tf  icf (TFICF)

and tf  idf  icf (TFICFIDF), are applied to centroid-
based classifiers. Four data sets are used for evaluation, i.e.,

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The term weighting of the prototype vector is tf  idf  icf . From the result, the function of class frequency, which ex-
pressed the effect on both promoting and demoting some
The rest of test documents are classified in the second step. terms was more effective. Moreover, increasing the exponent
Two term weightings are applied, i.e., tf  idf and of the icf , performance of classifiers is better on several
data sets. The other approach was to apply terms, which
tf  idf  icf . The result is shown in Table III. were found in only one class or few classes, to improve clas-
The result shows a lot improvement of performance from sification performance, using two-step classification. The
classifiers when two-step classification is applied. In the first result showed obviously that, a lot improvement of perfor-
step, average classification accuracy on the four data sets is mance from classifier over the single step classification. For
relatively high. Although performance in the first step of one- conclusion, class frequency expressed its usefulness in text
class terms representation is less than performance in two- classification.
class terms representation, the number of the rest documents For the future works, effect of class frequency on other
of one-class terms representation is larger than that of two- classification methods and performance of classifiers with
class terms representation. The consequence of this is the class frequency on cross data sets, should be evaluated.
number of documents that are assigned the correct class in
the second step from one-class terms representation, is larger ACKNOWLEDGMENT
than that of two-class terms representation. When the
classification process is completed, classification accuracy This work was funded by the Research and Development
from all documents in a collection, beginning with one-class Institute, Silpakorn Univeristy via research grant SURDI 53/
terms representation is higher than that of beginning with 01/12.
two-class terms representation. From the result, performance
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Using Class Frequency for Improving Centroid-based Text Classification

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