• Journal of Advanced Marine Engineering and Technology
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• v.34 no.6
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• pp.871-879
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• 2010

In this paper, we present a system that classifies comments on a news article into a user opinion called a polarity (positive or negative). The system is a kind of document classification system for comments and is based on machine learning techniques like support vector machine. Unlike normal documents, comments have their body that can influence classifying their opinions as polarities. In this paper, we propose a feature weighting scheme using such characteristics of comments and several resources for opinion classification. Through our experiments, the weighting scheme have turned out to be useful for opinion classification in comments on Korean news articles. Also Korean character n-grams (bigram or trigram) have been revealed to be helpful for opinion classification in comments including lots of Internet words or typos. In the future, we will apply this scheme to opinion analysis of comments of product reviews as well as news articles.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.12
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• pp.2609-2618
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• 2011

Under the knowledge-based economy in 21C, the convergence and complexity in science and technology are being more active. Interoperability between heterogeneous domains is a very important point considered in the field of scholarly information service as well information standardization. Thus we suggest the systematic solution method to flexibly extend classification scheme in order for content management and service organizations. Especially, This paper shows that automatic method for interoperability between heterogeneous scholarly classification code structures will be effective in enhancing the information service system.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.3
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• pp.112-119
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• 2001

This paper proposes an adaptive reclosing scheme which is based on the classification of fault patterns. In case that the first reclosing is unsuccessful in distribution system employing with two-shot reclosing scheme, the proposed method can determine whether the second reclosing will be attempted of not. If the first reclosing is unsuccessful two fault currents can be measured before the second reclosing is attempted, where these two fault currents are utilized for an adaptive reclosing scheme. Total harmonic distortion and RMS are used for extracting the characteristics of two fault currents. And the pattern of two fault currents is respectively classified using a mountain clustering method a minimum-distance classifier. Mountain clustering method searches the cluster centers using the acquired past data. And minimum-distance classifier is used for classifying the measured two currents into one of the searched centers respectively. If two currents have the different pattern it is interpreted as temporary fault. But in case of the same pattern, the occurred fault is interpreted as permanent. The proposed method was tested for the fault data which had been measured in KEPCO's distribution system, and the test results can demonstrate the effectiveness of the adaptive reclosing scheme.

• Journal of The Korean Association of Information Education
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• v.21 no.3
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• pp.297-307
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• 2017

The purpose of this study is to propose algorithm classification system for algorithm education for Elementary and Secondary Students. We defines the components of the algorithm and expresses the algorithm classification system by the analysis synthesis method. The contents of the study are as follows. First, we conducted a theoretical search on the classification purpose and classification. Second, the contents and limitations of the classification system for the proposed algorithm contents were examined. In addition, we examined the contents and selection criteria of algorithms used in algorithm education research. Third, the algorithm components were redefined using the core idea and crosscutting concept proposed by the NRC. And the crosscutting concept of algorithm is subdivided into algorithm data structure and algorithm design strategy, and its contents are presented using analytic synthesis classification scheme. Finally, the validity of the proposed contents was verified by the review of the expert group. It is expected that the study on the algorithm classification system will provide many implications for the contents selection and training method in the algorithm education.

• Journal of the Korean Society for Library and Information Science
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• v.40 no.4
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• pp.333-366
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• 2006

The purpose of this study is principle and theory of main class in a Library Classification. According to Sayers, 'The foundation of the library is the book; the foundation of librarianship is classification.' We looked at the between scientific and bibliographic classification, and at the fact that bibliographic scheme is usually an aspect classification. That is to say, the organization of topics is based on areas or activity and the first division of the scheme is into disciplines or subject domains. This first division of classification creates what are called main class. The sequence of main classes is also important. A rough definition of a amin class is that it corresponds to a sin91e notational character. Main classes usually equivalent to traditional disciplines. What constitutes a main class will vary from one classification to another. The order in which the main classes are listed is often discussed at the theoretical level, and some orders are considered to be better than others.

• Journal of The Geomorphological Association of Korea
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• v.19 no.3
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• pp.1-22
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• 2012

The classification scheme of estuaries can be divided into two categories: qualitative classification based on geomorphic characteristics and quantitative classification based upon the physical properties of water body. While simple and intuitive scheme of the former is difficult to quantify, the latter is not easy to apply due to the lack of data. A classification scheme based on morphological convergence is very promising because it only requires easily accessible data such as width and depth of channels, as well as it can characterize estuaries in terms of tidal propagation. Thus, this paper examines the classification scheme based on estuarine morphological convergence using depth and width data obtained from 19 major Korean estuaries. Morphological convergence for each estuary was estimated with the estuarine length, width and depth data to get the convergence parameters, which includes the degree of funneling ${\nu}$ and the dimensionless estuarine length $y_0$. The transfer function ${\xi}({\nu},ky)$ is then deduced analytically from 1D depth-integrated hydrodynamic momentum equation and continuity equation for estuarine shapes. Tidal response of each estuary is finally calculated using ${\nu}$, $y_0$ and ${\xi}({\nu},ky)$ for comparison and classification. The 19 Korean estuaries were classified into three groups: tidal amplitude-dominated estuaries with standing wave-like tidal response (group 1), current-dominated estuaries with progressive wave-like tidal response (group 2), and the intermediate group (group 3) between groups 1 and 2. The sensitivity analysis revealed that uncertainties in determining the estuarine length can have a critical effect upon the results of classification, which indicates that the reasonable determination of the estuarine length is of critical importance. Once the estuarine length is feasibly determined, depth-convergence can be neglected without any negative effect on the classification scheme, which has an important ramification on the wide applicability of the classification scheme.

• Journal of the Korean Society for Library and Information Science
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• v.20
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• pp.35-63
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• 1991

DDC, LCC, and CC can be said as the major schemes for mordern general library classification. Among these, DDC, since its publication in 1876, has been continuously studies and revised by many scholars and practitioners to publish 20th edition in 1989: LCC also has been studied and revised by the specialists in each subject, since 1904; and CC(first edition 1933) is now on the stage of 7th edition(1987). Even though studied, revised and developed by many classificationists, all these schemes maintain the general framework of the beginning, only with the partial revision and expansion to reflect the developments of the subjects. and antioipated tremendous amount of works resulted from reclassification also can be a reason that disturbs the full innovative revision of the scheme, because these are used in many libraries as a basic tools for the classification. But all these schemes mainly based on the state of the discipline at the time of their creation, the beginning of 20the century, and so in some aspect it is natural for them to have many problems. This study aims to investigate the problems in these major schemes, to find some ways to solve the problems, and to suggest the ideas for the basic design of a new modern library classification scheme. This plan is prepared to be applied to the situation of all countries equally without any revision. And in its notation, it uses two digits of Arabic numerals as centesimal, and so it is named provisionally to Plan for Centesimal Classification (PCC).

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.394-399
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• 2002

In this paper, Multisource data classification methods based on Bayesian formula are considered. For this decision fusion scheme, the individual data sources are handled separately by statistical classification algorithms and then Bayesian fusion method is applied to integrate from the available data sources. This method includes the combination of each expert decisions where the weights of the individual experts represent the reliability of the sources. The reliability measure used in the statistical approach is common to all pixels in previous work. In this experiment, the weight factors have been assigned to have different value for all pixels in order to improve the integrated classification accuracies. Although most implementations of Bayesian classification approaches assume fixed a priori probabilities, we have used adaptive a priori probabilities by iteratively calculating the local a priori probabilities so as to maximize the posteriori probabilities. The effectiveness of the proposed method is at first demonstrated on simulations with artificial and evaluated in terms of real-world data sets. As a result, we have shown that Bayesian statistical fusion scheme performs well on multispectral data classification.

• Journal of the Korean Society for information Management
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• v.34 no.4
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• pp.33-57
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• 2017

This study aims to introduce an emerging prescriptive analytics method and suggest its efficient application to a category-based service system. Prescriptive analytics method provides the whole process of analysis and available alternatives as well as the results of analysis. To simulate the process of optimization, large scale journal articles have been collected and categorized by classification scheme. In the process of applying the concept of prescriptive analytics to a real system, we have fused a dynamic automatic-categorization method for large scale documents and intellectual structure analysis method for scholarly subject fields. The test result shows that some optimized scenarios can be generated efficiently and utilized effectively for reorganizing the classification-based service system.

• Korean Journal of Cognitive Science
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• v.19 no.4
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• pp.477-497
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• 2008

Mood classification of blog text is an interesting problem, with a potential for a variety of services involving the Web. This paper introduces an approach to mood classification enhancements through the normalized negation n-grams which contain mood clues and corpus-specific term weighting(CSTW). We've done experiments on blog texts with two different classification methods: Enhanced Mood Flow Analysis(EMFA) and Support Vector Machine based Mood Classification(SVMMC). It proves that the normalized negation n-gram method is quite effective in dealing with negations and gave gradual improvements in mood classification with EMF A. From the selection of CSTW, we noticed that the appropriate weighting scheme is important for supporting adequate levels of mood classification performance because it outperforms the result of TF*IDF and TF.