• The KIPS Transactions:PartB
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• v.11B no.5
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• pp.545-554
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• 2004

Combining multiple classifiers to obtain improved performance over the individual classifier has been a widely used technique. The task of constructing a multiple classifier system(MCS) contains two different Issues how to generate a diverse set of base-level classifiers and how to combine their predictions. In this paper, we review the characteristics of existing multiple classifier systems : Bagging, Boosting, and Slaking. For document classification, we propose new MCSs such as Stacked Bagging, Stacked Boosting, Bagged Stacking, Boosted Stacking. These MCSs are a sort of hybrid MCSs that combine advantages of existing MCSs such as Bugging, Boosting, and Stacking. We conducted some experiments of document classification to evaluate the performances of the proposed schemes on MEDLINE, Usenet news, and Web document collections. The result of experiments demonstrate the superiority of our hybrid MCSs over the existing ones.

• The Korean Journal of Applied Statistics
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• v.32 no.2
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• pp.265-276
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• 2019

The document-term frequency matrix is a general data of objects in text mining. In this study, we introduce a traditional term weighting scheme TF-IDF (term frequency-inverse document frequency) which is applied in the document-term frequency matrix and used for text classifications. In addition, we introduce and compare TF-IDF-ICSDF and TF-IGM schemes which are well known recently. This study also provides a method to extract keyword enhancing the quality of text classifications. Based on the keywords extracted, we applied support vector machine for the text classification. In this study, to compare the performance term weighting schemes, we used some performance metrics such as precision, recall, and F1-score. Therefore, we know that TF-IGM scheme provided high performance metrics and was optimal for text classification.

• The KIPS Transactions:PartB
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• v.12B no.6 s.102
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• pp.697-702
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• 2005

The focused crawler is a topic-driven document-collecting crawler that was suggested as a promising alternative of maintaining up-to-date web document Indices in search engines. A major problem inherent in previous focused crawlers is the liability of missing highly relevant documents that are linked from off-topic documents. This problem mainly originated from the lack of consideration of structural information in a document. Traditional weighting method such as TFIDF employed in document classification can lead to this problem. In order to improve the performance of focused crawlers, this paper proposes a scheme of locality-based document segmentation to determine the relevance of a document to a specific topic. We segment a document into a set of sub-documents using contextual features around the hyperlinks. This information is used to determine whether the crawler would fetch the documents that are linked from hyperlinks in an off-topic document.

• International Commerce and Information Review
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• v.6 no.3
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• pp.41-61
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• 2004

Today, most companies are needed to become interested on e-Biz and information management system. Especially, Data classification format system was very important for application to effective and efficiency management decision support. They should include main entry which consists of department, employee's name, title, publication date. Now, each company is using eleven different methods on data classification format system. In this paper finding result was as follows, in other words, general management document case using the nine date classification methods and special report management document ca se using the twodata classification methods. The aim of this study is to investigate problems that the present data classification format system has and some concerns that should be taken into account in case of the modification of the data classification system and change into a new one. This study is based on the survey in that the company managergave to 35 companies throughout the nation. As a result, the survey indicates that the crucial concerns of the participating managers are ineffective management information source and the duplication of data classification systems. This paper is the transcendental study the introduction of data classification format systems to business companies in Korea. This paper provided the fundamental data for the effective business process reengineering in business activity for management information.

• Journal of IKEEE
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• v.8 no.2 s.15
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• pp.186-194
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• 2004

Due to the exponential growth of information on the internet, it is getting difficult to find and organize relevant informations. To reduce heavy overload of accesses to information, automatic text classification for handling enormous documents is necessary. In this paper, we describe structure and implementation of a document classification system for web documents. We utilize SVM for documentation classification model that is constructed based on training set and its representative terms in a directory. In our system, SVM is trained and is used for document classification by using word set that is extracted from information and communication related web documents. In addition, we use vector-space model in order to represent characteristics based on TFiDF and training data consists of positive and negative classes that are represented by using characteristic set with weight. Experiments show the results of categorization and the correlation of vector length.

• The KIPS Transactions:PartD
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• v.12D no.5 s.101
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• pp.789-796
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• 2005

Because existing information retrieval systems, in particular library retrieval systems, use 'exact keyword matching' with user's query, they present user with massive results including irrelevant information. So, a user spends extra effort and time to get the relevant information from the results. Thus, this paper will propose SULRM a Retrieval Model using Subject Classification Table, User profile, and LSI(Latent Semantic Indexing), to provide more relevant results. SULRM uses document filtering technique for classified data and document ranking technique for non-classified data in the results of keyword-based retrieval. Filtering technique uses Subject Classification Table, and ranking technique uses user profile and LSI. And, we have performed experiments on the performance of filtering technique, user profile updating method, and document ranking technique using the results of information retrieval system of our university' digital library system. In case that many documents are retrieved proposed techniques are able to provide user with filtered data and ranked data according to user's subject and preference.

• The KIPS Transactions:PartB
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• v.8B no.6
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• pp.657-668
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• 2001

It is difficult that web documents are classified with exact user intention because existing document classification systems are based on word frequency number using single keyword. To improve this defect, first, we use keyword, a query, domain knowledge. Like explanation based learning, first, query is analyzed with knowledge based information and then structured user intention information is extracted. We use this intention tree in the course of existing word frequency number based document classification as user information and constraints. Thus, we can classify web documents with more exact user intention. In classifying document, structured user intention information is helpful to keep more documents and information which can be lost in the system using single keyword information. Our hybrid approach integrating user intention information with existing statistics and probability method is more efficient to decide direction and range of document category than existing word frequency approach.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.347-358
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• 2003

VSField-associated term is a single or compound word whose terms occur in any document, and which makes it possible to recognize a field of text by using common knowledge of human. For example, human recognizes the field of document such as or , a field name of text, when she encounters a word 'Pitcher' or 'election', respectively We Proposes an efficient construction method of field-associated terms (FTs) for specializing field to decide a field of text. We could fix document classification scheme from well-classified document database or corpus. Considering focus field we discuss levels and stability ranks of field-associated terms. To construct a balanced FT collection, we construct a single FTs. From the collections we could automatically construct FT's levels, and stability ranks. We propose a new extraction algorithms of FT's for document classification by using FT's concentration rate, its occurrence frequencies.

• Journal of Korean Library and Information Science Society
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• v.44 no.1
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• pp.27-47
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• 2013

This study investigated the classification performance of a kNN classifier using the feature selection methods based on document frequency(DF) and collection frequency(CF). The results of the experiments, which used HKIB-20000 data, were as follows. First, the feature selection methods that used high-frequency terms and removed low-frequency terms by the CF criterion achieved better classification performance than those using the DF criterion. Second, neither DF nor CF methods performed well when low-frequency terms were selected first in the feature selection process. Last, combining CF and DF criteria did not result in better classification performance than using the single feature selection criterion of DF or CF.

• Journal of KIISE:Software and Applications
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• v.31 no.9
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• pp.1204-1217
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• 2004

Numerous statistical and machine learning techniques have been studied for automatic text classification. However, because they train the classifiers using only feature vectors of documents, ambiguity between two possible categories significantly degrades precision of classification. To remedy the drawback, we propose a new method which incorporates relationship information of categories into extant classifiers. In this paper, we first perform the document classification using the k-NN classifier which is generally known for relatively good performance in spite of its simplicity. We employ the relationship information from an object-based thesaurus to reduce the ambiguity. By referencing various relationships in the thesaurus corresponding to the structured categories, the precision of k-NN classification is drastically improved, removing the ambiguity. Experiment result shows that this method achieves the precision up to 13.86% over the k-NN classification, preserving its recall.