An efficient-classification-model-for-unstructured-text-document

An Efficient Classification Model for Unstructured Text Document
Mowafy M1*, Rezk A2 and El-bakry HM2
1Department of Information Systems, Faculty of Computers and Information, Kafrelsheikh University, 33512, Egypt
2Department of Information Systems, Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt
*Corresponding author: Mowafy M, Department of Information Systems, Faculty of Computers and Information, Kafrelsheikh University, 33512,
Egypt, E-mail: mm_is_85@yahoo.com
Received date: January 22, 2018; Accepted date: February 16, 2018; Published date: February 20, 2018
Citation: Mowafy M, Rezk A, El-bakry HM (2018) An Efficient Classification Model for Unstructured Text Document. Am J Compt Sci Inform Technol
Vol.6 No.1: 16
Copyright: © 2018 Mowafy M, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which
permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Document classification has become an important field of
research due to the increase of unstructured text
documents available in digital form. It is considered one of
the key techniques used for organizing the digital data by
automatically assigning a set of documents into predefined
categories based on their content. Document classification
is a process that consists of a set of phases, each phase can
be accomplished using various techniques. Selecting the
proper technique that should be used in each phase affects
the efficiency of the text classification performance. The
aim of this paper is to present a classification model that
supports both the generality and the efficiency. It supports
the generality through following the logical sequence of the
process of classifying the unstructured text documents step
by step; and supports the efficiency through proposing a
compatible combination of the embedded techniques for
achieving better performance. The experimental results
over 20-Newgroups dataset have been validated using
statistical measures of precision, recall, and f-score. The
results have proven the capability of the proposed model to
significantly improve the performance.
Keywords: Text classification; Document classification
model; Unstructured document classification process;
Multinomial naive bayes classifier; TF-IDF
Introduction
The day by day rapid growth of the amount of text data and
the need for a well-defined methodology to analyze and classify
these voluminous data has drawn many communities' attention
to this kind of data which is known as unstructured data. This
phenomenon has made the importance of text classification
begins to spring up. Text document classification (TC) is the
process of assigning the text documents to one or more proper
category based on their content by building model through a
training data. This process is considered a supervised
classification technique, since a set of predefined labeled
documents is provided as training set.
There have been various issues in proper classification of
texts. The size of datasets presents a big challenge. High
Dimensionality, that is, a large number of attributes which
presents the problem of poor classifier performance since many
attributes are irrelevant. This problem can be handled through
the feature selection phase, which is considered a major
research area. Another issue is how to represent the features of
documents which can be done by different methods, such as a
binary representation (presence/absence), frequency of
occurrences, normalized frequency and many more, which also
is one of the research areas.
Among the various machine learning techniques used for text
classification, the naive bayes has always been the most popular
for many years. Because of its simplicity as well as its speed and
effectiveness in the classification task. Naive bayes belongs to
the family of simple probabilistic classifier based on the
impendent assumption that the value of one feature is different
always from the other feature values. With this background, text
document classifiers based on naive bayes technique have been
studied extensively by many researchers. In their naive bayes
classifiers, documents are presented as a binary feature vector
according to whether each word is present or absent.
This paper proposes a model that presents the logical
sequence of text document classification phases step by step, as
well as discusses the possibility of using the multinomial naive
bayes with Term Frequency-Inverse document frequency (TF-
IDF) method for text classification. TF-IDF is a numerical statistic
method which calculates the weight for each term in each
document. This method evaluates the importance of terms in
document collections.
This paper is organized as follows: related work for text
classification techniques, especially the naive bayes and its
importance in section 2. Section 3 discusses the problem
statement. Section 4 presents the proposed model through
explaining the general architecture and explaining its major
phases. Section 5 provides the experimental analysis of the
proposed model. Section 6 concludes the work. Section 7
presents future trends in which the proposed work can exceed.
Research Article
iMedPub Journals
http://www.imedpub.com/
DOI: 10.21767/2349-3917.100016
Vol.6 No.1:16
2018
© Under License of Creative Commons Attribution 3.0 License | This article is available from: https://www.imedpub.com/computer-science-and-information-
technology/
1
American Journal of Computer Science and Information Technology
ISSN 2349-3917

Related Work
Text document classification studies have become an
emerging field in the text mining research area. Consequently,
an abundance of approaches has been developed for such
purpose, including support vector machines (SVM) [1], K-nearest
neighbor (KNN) classification [2], Naive Bayes classification [3],
Decision tree (DT) [4], Neural Network (NN) [5], and maximum
entropy [6]. In regard to these approaches, Multinomial Naive
Bayes test classifier has been vastly used due to its simplicity in
training and classifying phases [3]. Many researchers proved its
effectiveness in classifying the unstructured text documents in
various domains.
Dalal and Zaveri [7] have presented a generic strategy for
automatic text classification, which includes phases such as
preprocessing, feature selection, using semantic or statistical
techniques, and selecting the appropriate machine learning
techniques (Naive bayes, Decision tree, hybrid techniques,
Support vector machines). They have also discussed some of the
key issues involved in text classification such as handling a large
number of features, working with unstructured text, dealing
with missing metadata, and selecting a suitable machine
learning technique for training a text classifier.
Bolaj and Govilkar [8] have presented a survey of text
categorization techniques for Indian regional languages and
have proved that the naive bayes, k-nearest neighbor, and
support vector machines are the most suitable techniques for
achieving better document classification results for Indian
regional languages. Jain and Saini [9] used a statistical approach
to classify Punjabi text. In this paper Naive bayes classifier has
been successfully implemented and tested. The classifier has
achieved satisfactory results in classifying the documents.
Tilve and Jain [10] used three text classification algorithms
(Naive Bayes, VSM for text classification, and the new
implemented Use of Stanford Tagger for text classification) for
text classification on two different datasets (20 Newsgroups and
New news dataset for five categories). Comparing with the
above classification strategies, Naïve Bayes is potentially good at
serving as a text classification model due to its simplicity. Gogoi
and Sarma [11] has highlighted the performance of employing
Naive bayes technique in document classification.
In this paper, a classification model has been built on and
evaluated according to a small dataset of four categories and
200 documents for training and testing. The result has been
validated using statistical measures of precision, recall and their
combination F-measure. Results showed that Naïve Bayes is a
good classifier. This study can be extended by applying Naive
Bayes classification on larger datasets. Rajeswari R, et al. [12]
focused on text classification using Naive bayes and K-nearest
neighbor classifiers and to confirm on performance and accuracy
of these approaches. The result showed that Naive bayes
classifier is a good classifier with an accuracy of 66.67 as
opposed to KNN classifier with 38.89.
One closely related research paper to our research was of
Trstenjak B, et al. [13] that has proposed a text categorization
framework based on using KNN technique with TF-IDF method.
Both KNN and TF-IDF embedded together gave good results and
confirmed the initial expectations. The framework has been
tested on several categories of text Documents. During testing,
classification gave accurate results due to KNN algorithm. This
combination gives better results and needs to upgrade and
improve the framework for better and high accuracy results.
The Proposed Model
The proposed model is expected to achieve more efficiency in
the text classification performance. Hence, the proposed model
has been presented in [13] depends on using KNN with TF-IDF.
Our work used the multinomial naive bayes technique according
to its popularity and efficiency in text classification nowadays as
discussed earlier. Especially, its superior performance than K-
nearest neighbor as [12] has been illustrated. The proposed
model presents combining multinomial naive bayes as a selected
machine learning technique for classification, and TF-IDF as a
vector space model for text extraction, and chi2 technique for
feature selection for more accurate text classification with better
performance in comparison to the testing result to of [13]
framework.
The general steps for building Classification model as
presented in Figure 1 are: Preprocessing for all labeled and
unlabeled documents. Training, in which the classifier is
constructed from the labeled training prepared instances. Then,
the testing that is responsible for testing the model by testing
prepared samples whose class labels are known but not used for
training model. Finally, usage phase, since the model is prepared
to be used for classification of new prepared data whose class
label is unknown.
Figure 1: Model Architecture
Pre-processing
This phase is applied on the input documents, either on the
labeled data for training and testing phase or on the unlabeled
for the usage phase. This phase is used to present the text
documents in a clear word format. The output documents are
prepared for next phases in text classification. Commonly the
steps taken are:
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Tokenization: A document is treated as a string, and then
partitioned into a list of tokens.
Removing stop words: Stop words such as “the”, “a”, “and”,
etc. are frequently occurring, so the insignificant words need to
be removed.
Stemming word: Applying the stemming algorithm that
converts different word forms into a similar canonical form. This
step is the process of conflating tokens to their root form, e.g.
connection to connect, computing to compute.
Training phase
A set of labeled text documents, which is already prepared in
the Preprocessing phase,is used as input to this phase. This
phase is responsible for learning the classifier model. The output
of this phase is a trained classifier which is ready for testing and
classifying. Figure 2 illustrates the training phase.
Figure 2: Training Phase
Text document representation: Document representation is
the process of presenting the words and their number of
occurrences in each document. The two main approaches for
this process are Bag of words (BOW)and Vector Space. BOW, in
which each word is represented as a separate variable having
numeric such as term binary, term frequency or term frequency-
inverse document frequency [14]. Vector Space, which is used in
the text documents as vectors of identifiers [15]. Our model uses
the BOW for representing the text documents using the term
frequency weighting schema; while Vector Space will be used in
a separate phase. Term Frequency computes the number of
occurrences of a term ti in the document j.
tij=fij=frequency of term in document j → (1)
This phase should be finished by creating weigh matrix
�1 �2 .... ��
�1 �11 �11 .... �1� �1
�2 �21 �22 .... �2� �2
: ⋮ : : : :
�� 1 ��2 .... ��
Where each entry Wij represents the weight of term i in the
document j. There are several ways of determining the weight
Wij. The selected one of our model is Tij which is implemented
according to the following pseudo code:
Feature selection: Despite removing the stop words and
replacing each term by its stem in the preprocessing phase, the
number of words in a weight matrix created in the text
representation phase is still very large. Therefore, the feature
selection phase is applied for reducing the dimensionality of the
feature set by removing the irrelevant features. The goal of this
phase is improving the efficiency of classification accuracy as
well as reducing the computational requirements. Feature
selection is performed by keeping the features with highest
score according to the features' importance [16]. The most
commonly used methods for features' evaluation include
Information Gain, Mutual Information, Chi square statistic, and
latent semantic analysis. The proposed model uses Chi square
statistic method to be applied in this phase based on the term
frequency weight matrix created during the previous phase. Chi
square is the selected technique used to measure the
correlation between term ti and class Ck [17].
Let a be the number of documents with term and belong to a
category ck, b be the number of documents with term and do
not belong to the category, c be the number of documents
without term and belong to the category, d be the number of
documents without term and do not belong to the category.
Thus, N is the number of documents in the training set, N=a+b+c
+d.
�2
��, �� =
� * �� − ��
2
� + � * � + � * � + � * � + �
2
If X2(ti,ck)=0, the term ti and category ck are independent;
therefore, the term i doesn't contain any category information.
Otherwise, the greater the value of the X2(ti,ck), the more
contained category information of the term ti. The score of term
ti in the text collection is obtained by maximizing the category-
specific scores.
�2
max �� = max� = 1
�
��, �� 3
The following pseudo code present Chi square test:
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2018
© Under License of Creative Commons Attribution 3.0 License 3

Constructing vector space model: Once the feature selection
phase has been done and the best M features have been
selected, the vector space model will be constructed. That
vector space represents each document as a vector with M
dimensions, which is the number of selected features set that
has been produced in the previous phase, each vector can be
written as:
Vd=[W1d,W2d,……..,Wmd]
where Wid is a weight for measuring the importance of the
term i in document j. There are various methods that can be
used for weighting the terms as we mentioned in text
presentation phase. Our model uses TF-IDF, which stands for
Term Frequency-Inverse Document Frequency. TF-IDF can be
calculated as [17]:
�� = �� * �� = �� * log
�
��
�
4
Where Wij is the weight of term i in document j, N is the
number of all documents in the training set, tfij is the term
frequency for term i in document j, dfi is the document
frequency of term i in the documents of the training set. The
following pseudo code present TF-IDF vector space model:
Training classifier: Training classifier is the key component of
the text classification process. The role of this phase is to build a
classifier or generate model by training it using predefined
documents that will be used to classify unlabelled documents.
The Multinomial naive bayes classifier is selected to build the
classifier in our model.
The following probability calculations should be done during
this phase:
A. For each term or feature in the selected feature set,
calculate the probability of that feature (term) to each class.
Assume that a set of classes is denoted by C, where
C={c1,c2.......,ck} is the set of class labels, and N is the length of
the selected features set {t1,t2,…..tN} according to the use of
term frequencies to estimate the class-conditional probabilities
in the multinomial model [18]:
� ��/�� =
∑��
�
, � ∈ �
�
+ �
∑�
� ∈ ��
+ � . �
5
it can be adapted for using with tf-idf for our model, as the
following equation:
� ��/�� =
∑��
�
, � ∈ �� + �
∑�
� ∈ ��
+ � . �
6
Where:
• ti: A word from the feature vector t of a particular sample.
•
∑�� , � ∈ �� : The sum of raw tf-idf of word ti from
all documents in the training sample that belong to a class
ck.∑�� ∈ ��: The sum of all tf-idf in the training dataset
for class ck
• α: An additive smoothing parameter (α:=1 for Laplace
smoothing).
• V: The size of the vocabulary (number of different words in
the training set).
The pseudo code for this calculation step is:
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The output of this pseudo code should be in the following
form:
�1 �2 .... ��
�1 � �1/�1 � �1/�2 .... � �1/��
�2 � �2/�1 � �2/�2 .... � �2/��
: ⋮ : : :
�� /�1 � ��/�2 .... � ��/��
B. Calculate the prior probability for each class. Let Dc, be the
number of documents in each class, D be the total number of
documents in the training set. P(ck) is calculated using the
equation:
�(��) =
�
�
�
7
Testing phase
This phase is responsible for testing the performance of the
trained classifier and evaluating its capability for the usage. The
Main inputs of this phase are the trained classifier from the
previous phase and labeled testing documents; these
documents are divided into:
• Unlabeled documents which were prepared during the
preprocessing phase as input for the classification step.
• Their associated class labels which were used as input for the
evaluation step. Figure 3 shows the details of the testing
phase.
Figure 3: Testing Phase
Classification: This step is responsible for assigning an
unlabeled document to the correct class of that document. For
determining which class that the document d is assigned to, it's
required to calculate the probability of assigning that document
to each class. Then the class whose highest probability is the
correct one. The probability of the document d in class ck is
calculated according to [19] using the equation:
p(ck/d)=p(d/ck)*p(ck) → (8)
where p(ck) is the prior probability, which is calculated by
equation (7). The document d is split through the preprocessing
phase into a set of terms {t1,t2,…,tn}.
p(d/ck)= p(t1/ck)* p(t2/ck)…..* p(tn/ck) → (9)
where p(ti/ck) is calculated using equation (6)
p(ck/d)= p(t1/ck)* p(t2/ck)…..* p(tn/ck)*p(ck) → (10)
� ��/� = arg max
�
�
∈ �
� �� ∏� = 1
�
� ��/�� 11
Comparing each unique term in the terms of the test
document with the terms' probability calculated in the training
phase, then calculating the probability of the document d in
each class using equation (11); the one with the highest
probability is the correct match. The pseudo code for selecting
the correct associated class is shown below:
Evaluation: This step is responsible for evaluating the
performance of the classifier. The input of this step is the
predicted class labels of the testing documents from the
classification step and the actual associated class labels. The
performance of a classifier is evaluated according to the results
of comparing the predicted class labels with the actual labels.
Usage phase
The classifier in this phase is successfully trained, tested and
evaluated and ready for classification of new data whose class
labels are unknown as shown in Figure 4.
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Figure 4: Usage Phase
Experiment Results
Dataset and setup considerations
The experiment is carried out using (20-Newgroups) collected
by Ken Lang. It has become one of the standard corpora for text
classification. 20 newsgroups dataset is a collection of 19,997
newsgroup document which are taken from Usenet news,
partitioned across 20 different categories. The training dataset
consists of 11998 samples, 60% of which is training, the rest 40%
is used for testing the classifier. Table 1 presents the used
environment properties. Experimental Environment
characteristics are shown in Table 1.
Table 1: Experimental environment
OS Windows 7
CPU Core(TM) I7-3630
RAM 8.00 GB
Language Python
Results
The result of the experiment is measuring a set of items as the
following:
Superiority of using the multinomial naive bayes MNB with
TFIDF than KNN: In this section, we have investigated the
compatibility's effectiveness of our choice of combining the TF-
IDF weight with multinomial naive bayes (MNB) and compare it
with combining the same weight algorithm with KNN [13] for
classifying the unstructured text documents. Performance is
evaluated in terms of Accuracy, Precision, Recall and F-score for
all the documents as shown in Table 2 and Figure 5. Run time for
both MNB and KNN with TFIDF is presented in Figure 6.
Accuracy, Precision, Recall and F-score for each category in both
techniques are illustrated in Table 3.
Table 2: Accuracy, precision, recall and F-measure of both
approaches
Results MNB-TFIDF KNN-TFIDF
Accuracy 0.87 0.71
Precision 0.88 0.72
Recall 0.87 0.71
F1-Score 0.87 0.71
Time 0.44 (ms) 18.99 (ms)
Figure 5: Comparison of performance results for using both
KNN and Multinomial Naive bayes(MNB)with TF-IDF
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Figure 6: Runtime for both KNN and MNB with TFIDF
Table 3: performance measures for each category in the testing
data set for both KNN and Multinomial Naive bayes (MNB) with
TF-IDF
Cat
No. of
docs
Precision Recall F1-score
KNN MNB KNN MNB KNN MNB
Cat.0 278 0.59 0.66 0.81 0.94 0.68 0.78
Cat.1 287 0.53 0.88 0.57 0.76 0.55 0.81
Cat.2 305 0.58 0.90 0.71 0.93 0.64 0.91
Cat.3 320 0.60 0.85 0.54 0.87 0.57 0.86
Cat.4 288 0.63 0.88 0.63 0.92 0.63 0.90
Cat.5 315 0.79 0.98 0.62 0.82 0.69 0.89
Cat.6 297 0.58 0.93 0.52 0.79 0.55 0.86
Cat.7 322 0.74 0.93 0.70 0.89 0.72 0.91
Cat.8 298 0.85 0.96 0.79 0.94 0.82 0.95
Cat.9 278 0.74 0.95 0.78 0.96 0.76 0.95
Cat.10 287 0.77 0.91 0.83 0.98 0.80 0.94
Cat.11 308 0.82 0.93 0.86 0.96 0.84 0.95
Cat.12 288 0.71 0.58 0.60 0.86 0.65 0.86
Cat.14 318 0.85 0.97 0.67 0.83 0.75 0.90
Cat.15 286 0.82 0.90 0.80 0.98 0.81 0.94
Cat.16 282 0.94 0.77 0.97 1 0.96 0.87
Cat.17 308 0.73 0.78 0.79 0.95 0.76 0.86
Cat.18 312 0.83 0.94 0.90 0.95 0.87 0.95
Cat.19 304 0.64 0.78 0.70 0.81 0.67 0.79
Cat.20 319 0.62 0.80 0.51 0.36 0.56 0.50
The compatibility of selecting the previous
evaluated choice (MNB-TFIDF) with the Chi square
as the selected technique for feature selection phase
in the proposed model
This section measures the selecting of the CHi-square (Chi2)
technique for applying in feature selection phase, through
illustrating its capability for improving the performance of the
previous evaluated MNB-TFIDF technique as shown in table 4
and Figure 7 that present Accuracy, Precision, Recall and F-score
for all the documents before and after χ2 and the associated
runtimes in Figure 8.
Table 4: Accuracy, precision, recall and F-measure of both
approaches
Results MNB-TFIDF MNB-TFIDF+χ2
Accuracy 0.87 0.91
Precision 0.88 0.92
Recall 0.87 0.91
F1-Score 0.87 0.92
Time 0.46 (ms) 0.02 (ms)
Figure 7: Comparison of performance results for using (MNB-
TFIDF) with x2 using the proposed model
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Figure 8: Runtime comparison for MNB-TFIDF with/without
Chi
The evaluation of the proposed model: Evaluating the whole
model through the precision, recall, f1-score performance
measures for each category in the testing data set as shown in
Table 5.
Table 5: performance measures for each category in the testing
data set
No. of
docs
Cat. 0 278 0.69 0.79 0.73
Cat. 1 287 0.92 0.91 0.91
Cat. 2 305 0.94 0.96 0.95
Cat. 3 320 0.86 0.95 0.90
Cat. 4 288 0.96 0.95 0.96
Cat. 5 315 0.94 0.90 0.92
Cat. 6 297 0.98 0.97 0.97
Cat. 7 322 0.96 0.97 0.96
Cat. 8 298 1 0.96 0.98
Cat. 9 278 1 0.97 0.99
Cat. 10 287 0.98 0.99 0.99
Cat. 11 308 0.98 0.96 0.97
Cat. 12 288 0.94 0.90 0.92
Cat. 13 318 0.93 0.96 0.95
Cat. 14 286 0.98 0.99 0.99
Cat. 15 282 0.97 1 0.98
Cat. 16 308 0.81 0.97 0.88
Cat. 17 312 0.92 0.92 0.92
Cat. 18 304 0.84 0.70 0.77
Cat. 19 319 0.72 0.59 0.65
Capability of the model to work with other techniques: This
part of the evaluation proves the capability of the model for
improving the quality of other classification techniques as in
Tables 6 and 7 and Figures 9 and 10.
Table 6: Accuracy, Precision, Recall and F-measure of both
approaches.
Results KNN-TFIDF KNN-TFIDF using the model
Accuracy 0.71 0.89
Precision 0.72 0.89
Recall 0.71 0.89
F1-Score 0.71 0.89
Time 0:00:33.681047 0:00:10.93001
Figure 9: Comparison of performance results for using
another technique with and without the proposed model
Table 7: Performance measures for each category in the testing data set for both KNN-TFIDF with and without the proposed model
Cat No. of docs
with without with without with without
Cat.0 278 0.64 0.59 0.80 0.81 0.71 0.68
Cat.1 287 0.89 0.53 0.89 0.57 0.89 0.55
Cat.2 305 0.92 0.58 0.95 0.71 0.93 0.64
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Cat.3 320 0.90 0.60 0.96 0.54 0.93 0.57
Cat.4 288 0.96 0.63 0.94 0.63 0.95 0.63
Cat.5 315 0.94 0.79 0.88 0.62 0.91 0.69
Cat.6 297 0.95 0.58 0.95 0.52 0.95 0.55
Cat.7 322 0.93 0.74 0.95 0.70 0.94 0.72
Cat.8 298 0.99 0.85 0.92 0.79 0.95 0.82
Cat.9 278 0.91 0.74 0.94 0.78 0.92 0.76
Cat.10 287 0.97 0.77 0.97 0.83 0.97 0.80
Cat.11 308 0.96 0.82 0.90 0.86 0.93 0.84
Cat.12 288 0.90 0.71 0.91 0.60 0.91 0.65
Cat.14 318 0.89 0.85 0.91 0.67 0.90 0.75
Cat.15 286 0.96 0.82 0.95 0.80 0.96 0.81
Cat.16 282 0.97 0.94 0.99 0.97 0.98 0.96
Cat.17 308 0.82 0.73 0.86 0.79 0.84 0.76
Cat.18 312 0.94 0.83 0.89 0.90 0.92 0.87
Cat.19 304 0.72 0.64 0.72 0.70 0.72 0.67
Cat.20 319 0.68 0.62 0.58 0.51 0.63 0.56
Figure 10: Runtime Comparison for KNN with and without the
model
Conclusions
In this paper, we present a model for text classification that
depicts the stream of phases through building automatic text
document classifier and presenting the relationship between
them. Evaluation of the proposed model was performed on 20-
Newgroups dataset. The experiment results confirmed that
firstly, superiority of using the Multinomial Naive bayes with
TFIDF than K-nearest neighbour as an approach for text
document classification. The proposed model demonstrates that
the proposed choice of embedded techniques in the model gave
better results, improved the classification process performance,
and confirmed our concept of the compatibility between the
selected techniques of various phases. Finally, despite the
proposed model performance is better, according to the
selected embedded techniques, it can improve the performance
of other techniques as shown in the last section of the
experimental results, since the accuracy of KNN-TFIDF [13] could
be improved using the proposed model from 71% to 76%.
Therefore, our model is extendable for using by other
approaches.
Future work
The proposed model provides the ability to upgrade through
studying many further compatible combinations of classification
techniques with different term weighting schemas and feature
selection techniques for better performance.
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