A Review of Multi-Label Text Categorization Using Classification and
Clustering Technique
Puneet Nema,
Vivek Sharma
puneetlnct92@gmail.com,
sharma.vivek.1512@gmail.com
Department of Information Technology
SATI Vidisha, M.P., India
SATI Vidisha M .P. India
Abstract
The increasing rate of data diversity in current decade faced a
problem of data categorization. Data categorization used a
classification technique such as KNN, decision tree and
support vector machine. The process of classification depends
on the similarity of features. The dependency of feature bound
the limitation of accuracy of classifier. The process of
classification mapped data into labels and labels categorized
in the different predefined class for the classification purpose.
In this paper present the review of classification and clustering
technique for multi-label text categorization.
Keywords: - Data Mining,
classification and clustering
Department of Information Technology
Text
categorization such as clustering and classification. The
processing of clustering used clustering technique such as kmeans, EM and FCM method. Impart of classification
technique used various model of classifier. The validation of
cluster data to classification technique used fuzzy transform
function to map class data to classifier data. Here in figure
shows that multi-level text categorization[7].
categorization,
Introduction
Multi-label text categorization plays an important role in
multiple documents searching in different domain. The
classification accuracy of multi-label text categorization is
major issue in machine learning technique[1]. For the
improvement of classification and clustering ratio used
different clustering and classification technique[2]. The major
issue in multi-label text categorization is similarity measure of
class and attributes value. For that improvement used various
feature selection based method. Different kinds of machine
learning algorithms, such as the KNN, support vector
machine, and logistic regression methods, have been proposed
to resolve such classification problem, and have achieved a
satisfactory level of classification[3,4]. Instead of that some
real-world problems, each instance could be associated with
multiple classes simultaneously. The process of classification
technique divided into three section, in first section design a
learning process, in second section design the testing phase
and in final section design the application phase. The classifier
builder built during the learning phase[5,6]. The form of
classifier builder is mathematical function model and
regression model. It may be in the form of classification
rules, a decision tree, or a mathematical formula. Some
authors used combined scheme for multi-level text
Figure 1: Multi label classification.
Multi-label classification refers to the task of learning a
function that maps various instances into one. This makes
multi-label data particularly interesting from the learning
perspective, since, in contrast to binary or multi-class
classification,
there
are
label
dependencies
and
interconnections in the data which can be detected and
exploited in order to obtain additional useful information or
just better classification performance. This paper is divided
into five sections. Section-I gives the introduction of multilabel text categorization and classification technique[8].
Section-II gives the information of related work in the field of
multi-label text categorization. In section-III discuss the
problem formulation of multi-label text categorization and
finally discuss conclusion and future work in section IV.
Related work
[In this section discuss the related work in the field of multilabel text categorization. For the improvements of multi-level
text categorization used various machine learning technique
such as clustering and classification technique. Some
technique of work is discussed here.
the simple and effective standard LP method, when faced with
domains with large number of labels and training examples.
1] In this paper author propose a fuzzy based method for
multi-label text classification in which a document can belong
to one or more than one category. In text categorization, the
number of the involved features is usually huge, causing the
curse of the dimensionality problem. Besides, a category can
be a nonconvex region, which is a union of several
overlapping or disjoint sub-regions. An automatic
classification system, thus, may suffer from large memory
requirements or poor performance. By incorporating fuzzy
techniques, our proposed method can overcome these issues.
[6] In this paper author propose TREEBOOST.MH, a multilabel HTC algorithm consisting of a hierarchical variant of
ADABOOST.MH, a very well-known member of the family
of ‘‘boosting’’ learning algorithms. TREEBOOST.MH
embodies several intuitions that had arisen before within
HTC: e.g. the intuitions that both feature selection and the
selection of negative training examples should be performed
‘‘locally’’, i.e. by paying attention to the topology of the
classification scheme. It also embodies the novel intuition that
the weight distribution that boosting algorithms update at
every boosting round should likewise be updated ‘‘locally’’.
[2] In this paper author contribute to such a multilevel
clustering theory, by designing and studying a multilevel
modularity measure for hierarchically clustered graphs,
explicitly taking the nesting structure of clusters into account.
The multilevel modularity we propose generalizes a
modularity measure in the context of reverse software
engineering. The measure they designed recursively traverses
the hierarchy of clusters and computes a one-variable
polynomial encoding the intra and inter-cluster densities
appearing at all levels in a hierarchical clustering. The
resulting polynomial reflects how the graph combines with the
hierarchy of clusters and can be used to assess the quality of a
hierarchical clustering.
[3] In this paper author present IVTURS, which is a new
linguistic fuzzy rule-based classification method based on a
new completely interval valued fuzzy reasoning method. This
inference process uses interval valued restricted equivalence
functions to increase the relevance of the rules in which the
equivalence of the interval membership degrees of the patterns
and the ideal membership degrees is greater, which is a
desirable behavior. Furthermore, their parameterized
construction allows the computation of the optimal function
for each variable to be performed, which could involve a
potential improvement in the system’s behavior.
[4] In this paper author aims to provide a timely review on this
area, with emphasis on state-of-the-art multi-label learning
algorithms. Firstly, fundamentals on multi-label learning
including formal definition and evaluation metrics are given.
Secondly and primarily, twelve representative multi-label
learning algorithms are scrutinized under common notations,
with corresponding analyses and discussions. Thirdly, several
extended topics on multi-label learning are briefly
summarized. As a conclusion, online resources and open
research problems on multi-label learning are outlined for
reference purposes.
[5] In this paper author presented a new multi-label
classification method, called RAkEL, that learns an ensemble
of LP classifiers, each one targeting a different small random
subset of the set of labels. The motivation was the computational efficiency and predictive performance problems of
[7] In this paper author study the problem of transductive
multilabel learning and propose a novel solution, called
TRAsductive Multilabel Classification (TRAM), to effectively
assign a set of multiple labels to each instance. Different from
supervised multilabel learning methods, they estimate the
label sets of the unlabeled instances effectively by utilizing
the information from both labeled and unlabeled data. They
first formulate the transductive multilabel learning as an
optimization problem of estimating label concept
compositions. Then, they derive a closed-form solution to this
optimization problem and propose an effective algorithm to
assign label sets to the unlabeled instances.
[8] In this paper author show that the Hybridization of EM
algorithm and SVM cluster combines the classification power
to produce the multi-label categorization results by removing
noise effectively. Initially, EM algorithm extracts the
potentially noisy article from the data set using the descending
porthole technique. Descending porthole is a sliding window
technique used from the top to bottom of the article for
preprocessing. Subsequently, SVM cluster establish the
content holdup method which generates a more efficient
multi-label representation of the articles. Hybridization of EM
algorithm and SVM cluster outperforms the Fuzzy SelfConstructing Feature Clustering Algorithm in terms of lexica
inclusion and multi-label categorization of text results.
[9] In this paper author presents two novel multi-label
classification algorithms based on the variable precision
neighborhood rough sets, called multi-label classification
using rough sets (MLRS) and MLRS using local correlation
(MLRS-LC). The proposed algorithms consider two important
factors that affect the accuracy of prediction, namely the
correlation among the labels and the uncertainty that exists
within the mapping between the feature space and the label
space. MLRS provides a global view at the label correlation
while MLRS-LC deals with the label correlation at the local
level. Given a new instance, MLRS deter-mines its location
and then computes the probabilities of labels according to its
location. The MLRS-LC first finds out its topic and then the
probabilities of new instance belonging to each class is
calculated in related topic.
[10] In this work author was involved with the task of multilabel classification. It introduced the problem, gave an
organized presentation of the methods that exist in the
literature, and provided comparative experimental results for
some of these methods. In the future they intend to perform a
finer-grained categorization of the different multi-label
classification methods and perform more extensive
experiments with more data sets and methods. They also
intend to perform a comparative experimental study of
problem adaptation methods.
REFERENCES:-
Problem Formulation
[3] Jose Antonio Sanz, Alberto Fernandez, Humberto
Bustince, Francisco Herrera “IVTURS: A Linguistic Fuzzy
Rule-Based Classification System Based On a New IntervalValued Fuzzy Reasoning Method With Tuning and Rule
Selection” IEEE TRANSACTIONS ON FUZZY SYSTEMS,
Vol-21, 2013. Pp 399-412.
The major challenges of the machine learning approach to text
classification is how to translate the textual information into
the features that eventually can be used by a machine learning
algorithm. This is what we refer to as feature generation.
Perhaps in an ideal world the true semantics of the text is
understood and only the relevant concepts are used as
features. In practice just using each word as a separate feature
already works quite well. However most approaches will
generate an enormous number of features, which not all
machine learning algorithms can handle well. In order for
them to work, only the most promising features are selected to
feed to the algorithm. In the process of review we found that
some performance affected problem related to the multi-label
classification. These problem are affected the performance
and accuracy of multi-level classifier and generate
unclassified region. The unclassified region increase, decrease
the accuracy and performance of classifier. Some problems
are mentioned here [4, 6, 9, 10].
1. Infinite population of data.
2. Feature selection of data
3. Voting of class
4. New class generation.
5. imbalanced data problem
6.
dependence of Label
Conclusion and future work
In this paper presents the review of multi-label text
categorization using classification and clustering technique.
for the clustering and classification used various technique of
classification and clustering. The major issue is feature
generation in text categorization for the process of clustering
and classification. In future improved multi-Label
classification technique based on TLBO algorithm. The TLBO
algorithm improved the accuracy of minority class of
classifier and reduces the unclassified region in multi-label
classification. The increasing of multi-label classification
region improved the accuracy and performance of classifier.
Increase the accuracy of classifier; Remove the dependency of
label, Reduces size of data, Decrease the feature dissimilarity
and used real time data for the classification.
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