Abstract: Text categorization is the problem of classifying text
documents into a set of predefined classes. After a preprocessing
step, the documents are typically represented as large sparse vectors.
When training classifiers on large collections of documents, both the
time and memory restrictions can be quite prohibitive. This justifies
the application of feature selection methods to reduce the
dimensionality of the document-representation vector. In this paper,
we present three feature selection methods: Information Gain,
Support Vector Machine feature selection called (SVM_FS) and
Genetic Algorithm with SVM (called GA_SVM). We show that the
best results were obtained with GA_SVM method for a relatively
small dimension of the feature vector.
Abstract: Text document categorization involves large amount
of data or features. The high dimensionality of features is a
troublesome and can affect the performance of the classification.
Therefore, feature selection is strongly considered as one of the
crucial part in text document categorization. Selecting the best
features to represent documents can reduce the dimensionality of
feature space hence increase the performance. There were many
approaches has been implemented by various researchers to
overcome this problem. This paper proposed a novel hybrid approach
for feature selection in text document categorization based on Ant
Colony Optimization (ACO) and Information Gain (IG). We also
presented state-of-the-art algorithms by several other researchers.
Abstract: Knowledge-based e-mail systems focus on
incorporating knowledge management approach in order to enhance
the traditional e-mail systems. In this paper, we present a knowledgebased
e-mail system called KS-Mail where people do not only send
and receive e-mail conventionally but are also able to create a sense
of knowledge flow. We introduce semantic processing on the e-mail
contents by automatically assigning categories and providing links to
semantically related e-mails. This is done to enrich the knowledge
value of each e-mail as well as to ease the organization of the e-mails
and their contents. At the application level, we have also built
components like the service manager, evaluation engine and search
engine to handle the e-mail processes efficiently by providing the
means to share and reuse knowledge. For this purpose, we present the
KS-Mail architecture, and elaborate on the details of the e-mail
server and the application server. We present the ontology mapping
technique used to achieve the e-mail content-s categorization as well
as the protocols that we have developed to handle the transactions in
the e-mail system. Finally, we discuss further on the implementation
of the modules presented in the KS-Mail architecture.
Abstract: Since dealing with high dimensional data is
computationally complex and sometimes even intractable, recently
several feature reductions methods have been developed to reduce
the dimensionality of the data in order to simplify the calculation
analysis in various applications such as text categorization, signal
processing, image retrieval, gene expressions and etc. Among feature
reduction techniques, feature selection is one the most popular
methods due to the preservation of the original features.
In this paper, we propose a new unsupervised feature selection
method which will remove redundant features from the original
feature space by the use of probability density functions of various
features. To show the effectiveness of the proposed method, popular
feature selection methods have been implemented and compared.
Experimental results on the several datasets derived from UCI
repository database, illustrate the effectiveness of our proposed
methods in comparison with the other compared methods in terms of
both classification accuracy and the number of selected features.
Abstract: Text categorization - the assignment of natural language documents to one or more predefined categories based on their semantic content - is an important component in many information organization and management tasks. Performance of neural networks learning is known to be sensitive to the initial weights and architecture. This paper discusses the use multilayer neural network initialization with decision tree classifier for improving text categorization accuracy. An adaptation of the algorithm is proposed in which a decision tree from root node until a final leave is used for initialization of multilayer neural network. The experimental evaluation demonstrates this approach provides better classification accuracy with Reuters-21578 corpus, one of the standard benchmarks for text categorization tasks. We present results comparing the accuracy of this approach with multilayer neural network initialized with traditional random method and decision tree classifiers.
Abstract: This paper introduces a measure of similarity between
two clusterings of the same dataset produced by two different
algorithms, or even the same algorithm (K-means, for instance, with
different initializations usually produce different results in clustering
the same dataset). We then apply the measure to calculate the
similarity between pairs of clusterings, with special interest directed
at comparing the similarity between various machine clusterings and
human clustering of datasets. The similarity measure thus can be used
to identify the best (in terms of most similar to human) clustering
algorithm for a specific problem at hand. Experimental results
pertaining to the text categorization problem of a Portuguese corpus
(wherein a translation-into-English approach is used) are presented, as well as results on the well-known benchmark IRIS dataset. The
significance and other potential applications of the proposed measure
are discussed.
Abstract: In text categorization problem the most used method
for documents representation is based on words frequency vectors
called VSM (Vector Space Model). This representation is based only
on words from documents and in this case loses any “word context"
information found in the document. In this article we make a
comparison between the classical method of document representation
and a method called Suffix Tree Document Model (STDM) that is
based on representing documents in the Suffix Tree format. For the
STDM model we proposed a new approach for documents
representation and a new formula for computing the similarity
between two documents. Thus we propose to build the suffix tree
only for any two documents at a time. This approach is faster, it has
lower memory consumption and use entire document representation
without using methods for disposing nodes. Also for this method is
proposed a formula for computing the similarity between documents,
which improves substantially the clustering quality. This
representation method was validated using HAC - Hierarchical
Agglomerative Clustering. In this context we experiment also the
stemming influence in the document preprocessing step and highlight
the difference between similarity or dissimilarity measures to find
“closer" documents.
Abstract: Text categorization (the assignment of texts in natural language into predefined categories) is an important and extensively studied problem in Machine Learning. Currently, popular techniques developed to deal with this task include many preprocessing and learning algorithms, many of which in turn require tuning nontrivial internal parameters. Although partial studies are available, many authors fail to report values of the parameters they use in their experiments, or reasons why these values were used instead of others. The goal of this work then is to create a more thorough comparison of preprocessing parameters and their mutual influence, and report interesting observations and results.
Abstract: Text categorization is the problem of classifying text
documents into a set of predefined classes. In this paper, we
investigated three approaches to build a meta-classifier in order to
increase the classification accuracy. The basic idea is to learn a metaclassifier
to optimally select the best component classifier for each
data point. The experimental results show that combining classifiers
can significantly improve the accuracy of classification and that our
meta-classification strategy gives better results than each individual
classifier. For 7083 Reuters text documents we obtained a
classification accuracies up to 92.04%.
Abstract: Text categorization is the problem of classifying text
documents into a set of predefined classes. After a preprocessing
step, the documents are typically represented as large sparse vectors.
When training classifiers on large collections of documents, both the
time and memory restrictions can be quite prohibitive. This justifies
the application of feature selection methods to reduce the
dimensionality of the document-representation vector. In this paper,
three feature selection methods are evaluated: Random Selection,
Information Gain (IG) and Support Vector Machine feature selection
(called SVM_FS). We show that the best results were obtained with
SVM_FS method for a relatively small dimension of the feature
vector. Also we present a novel method to better correlate SVM
kernel-s parameters (Polynomial or Gaussian kernel).
Abstract: Text categorization is the problem of classifying text documents into a set of predefined classes. After a preprocessing step the documents are typically represented as large sparse vectors. When training classifiers on large collections of documents, both the time and memory restrictions can be quite prohibitive. This justifies the application of features selection methods to reduce the dimensionality of the document-representation vector. Four feature selection methods are evaluated: Random Selection, Information Gain (IG), Support Vector Machine (called SVM_FS) and Genetic Algorithm with SVM (GA_FS). We showed that the best results were obtained with SVM_FS and GA_FS methods for a relatively small dimension of the features vector comparative with the IG method that involves longer vectors, for quite similar classification accuracies. Also we present a novel method to better correlate SVM kernel-s parameters (Polynomial or Gaussian kernel).
Abstract: Text categorization techniques are widely used to many Information Retrieval (IR) applications. In this paper, we proposed a simple but efficient method that can automatically find the relationship between any pair of terms and documents, also an indexing matrix is established for text categorization. We call this method Indexing Matrix Categorization Machine (IMCM). Several experiments are conducted to show the efficiency and robust of our algorithm.