• Asia pacific journal of information systems
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• v.14 no.2
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• pp.115-130
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• 2004

This paper proposes how to build an expert profile database in KMS, which provides the information of expertise that each expert possesses in the organization. To manage tacit knowledge in a knowledge management system, recent researches in this field have shown that it is more applicable in many ways to provide expert search mechanisms in KMS to pinpoint experts in the organizations with searched expertise so that users can contact them for help. In this paper, we develop a framework to automate expert classification using a text categorization technique called Vector Space Model, through which an expert database composed of all the compiled profile information is built. This approach minimizes the maintenance cost of manual expert profiling while eliminating the possibility of incorrectness and obsolescence resulted from subjective manual processing. Also, we define the structure of expertise so that we can implement the expert classification framework to build an expert database in KMS. The developed prototype system, "Knowledge Portal for Researchers in Science and Technology," is introduced to show the applicability of the proposed framework.

• Journal of information and communication convergence engineering
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• v.7 no.3
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• pp.351-355
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• 2009

The amount of incoming e-mails is increasing rapidly due to the wide usage of Internet. We often group e-mails into categories for maintaining e-mail efficiently. However reading the email messages and classifying them is still tedious task. Moreover, the number of e-mails and manual classifying is increasing everyday. So, automatic e-mail classification is important techniques. In this paper, we propose a multi-way e-mail classification method that uses PCA for automatic category generation and dynamic category hierarchy for re-organizing e-mail categories. It classifies a huge amount of receiving e-mail messages automatically, efficiently, and accurately.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.11b
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• pp.1077-1080
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• 2003

본 연구는 룰들의 각 필드들을 index하여 곱한 cross-product 테이블을 이용한 packet classification 알고리즘에 대해 연구하고 그 것의 성능을 평가하고 분석한다. 현재 Packet Classification은 Packet Filtering, Policy Routing, Accounting & Billing, Traffic Rate Limiting, Traffic Shaping, 등등의 서비스를 위한 가장 핵심적인 작업이다. 그러나 이들을 빠르게 서비스하는 알고리즘은 아직 존재하지 않는다. 단지 하드웨어 TCAM 을 이용해서 작은 룰들에 대한 처리만이 어느 정도 가능한 실정이다. 이에 본 연구는 소프트웨어를 이용한 cross-product 알고리즘의 효용성을 가늠하고자 연구하고 이를 실제 구현해 평가하고자 한다.

• Journal of the Korean Data and Information Science Society
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• v.15 no.3
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• pp.565-574
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• 2004

Classification tree algorithms including as CART by Brieman et al.(1984) in some aspects, recursively partition the data space with the aim of making the distribution of the class variable as pure as within each partition and consist of several steps. SUPPORT(smoothed and unsmoothed piecewise-polynomial regression trees) method of Chaudhuri et al(1994), a weighted averaging technique is used to combine piecewise polynomial fits into a smooth one. We focus on applying SUPPORT to a binary class variable. Logistic model is considered in the caculation techniques and the results are shown good classification rates compared with other methods as CART, QUEST, and CHAID.

• Journal of the Korea Society of Computer and Information
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• v.21 no.9
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• pp.11-18
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• 2016

In this paper, we propose a gas classification method using combined features for an electronic nose system that performs well even when some loss occurs in measuring data samples. We first divide the entire measurement for a data sample into three local sections, which are the stabilization, exposure, and purge; local features are then extracted from each section. Based on the discrimination analysis, measurements of the discriminative information amounts are taken. Subsequently, the local features that have a large amount of discriminative information are chosen to compose the combined features together with the global features that extracted from the entire measurement section of the data sample. The experimental results show that the combined features by the proposed method gives better classification performance for a variety of volatile organic compound data than the other feature types, especially when there is data loss.

• Korean Journal of Remote Sensing
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• v.25 no.3
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• pp.255-261
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• 2009

A new polarimetric SAR image classification technique based on the degree of polarization (DoP) and the co-polarized phase-difference (CPD) is presented in this paper. Since the DoP and the CPD of a scattered wave provide information on the randomness of the scattering and the type of scattering mechanisms, at first, the statistics of the DoP and CPD are examined with measured polarimetric SAR image data. Then, a DoP-CPD diagram with appropriate boundaries between six different classes is developed based on the SAR image. The classification technique is verified using the JPL AirSAR and ALOS PALSAR polarimetric data. The technique may have capability to classify an SAR image into six major classes; a bare surface, a village, a crown-layer short vegetation canopy, a trunk-layer short vegetation canopy, a crown-layer forest, and a trunk-dominated forest.

• 한국데이터정보과학회:학술대회논문집
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• 2002.06a
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• pp.23-28
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• 2002

Support vector machine(SVM) has been very successful in classification, regression, time series prediction and density estimation. In this paper, we will propose SVM for fuzzy data classification.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.3
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• pp.76-88
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• 2015

This paper presents a classification method based on the combination of active learning with temporal contextual information extracted from past land-cover maps for the classification of crop cultivation areas. Iterative classification based on active learning is designed to extract reliable training data and cultivation rules from past land-cover maps are quantified as temporal contextual information to be used for not only assignment of training data but also relaxation of spectral ambiguity. To evaluate the applicability of the classification method proposed in this paper, a case study with MODIS time-series vegetation index data sets and past cropland data layers(CDLs) is carried out for the classification of corn and soybean in Illinois state, USA. Iterative classification based on active learning could reduce misclassification both between corn and soybean and between other crops and non crops. The combination of temporal contextual information also reduced the over-estimation results in major crops and led to the best classification accuracy. Thus, these case study results confirm that the proposed classification method can be effectively applied for crop cultivation areas where it is not easy to collect the sufficient number of reliable training data.

• 한국데이터정보과학회:학술대회논문집
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• 2003.10a
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• pp.123-123
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• 2003

More elaborated methods allowing the usage of binary classifiers for the resolution of multi-class classification problems are briefly presented. This way of using FSVC to learn a K-class classification problem consists in choosing the maximum applied to the outputs of K FSVC solving a one-per-class decomposition of the general problem.

• International Commerce and Information Review
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• v.16 no.1
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• pp.91-116
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• 2014

This study reviews the methodology for utilizing information technology by which even non-professionalists in FTAs and commodity classification area can perform the determination of commodity classification, with ease and by themselves, by means of easy utilization of the information on commodity classification and FTAs, of importing and exporting goods. This article examines the technological elements and logics, etc. which simulate the commodity classification for utilizing FTAs. To achieve this, the author has developed the technology to support the determination of commodity classification numbers by accumulating the database of examples for classification after analyzing the classification factors by each commodity item. Utilizing this Commodity Classification Determination Supporting System, users can enjoy effects of education as well as consulting. In this regards, the advantages of this system can be enumerated as followings : Firstly, self-checking on commodity classification can be performed. Secondly, time and cost for classification can be saved. Thirdly, comprehensive competitiveness will be enhanced by allowing traders to achieve the benefit of FTA preferential tariff, for they will be able to issue the Certificate of Origins on a more accurate and precise basis of commodity classification.