• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2005년도 추계종합학술대회
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• pp.383-386
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• 2005

This study proposes a case based reasoning system with two dimensional reduction techniques. In this study, vertical and horizontal dimensions of the research data are reduced through hybrid feature and instance selection process using genetic algorithms. We applied the proposed model to customer classification model which utilizes customers' demographic characteristics as inputs to predict their buying behavior for the specific product. Experimental results show that the proposed technique may improve the classification accuracy and outperform various optimized models of typical CBR system.

• 대한안전경영과학회지
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• 제20권1호
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• pp.41-55
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• 2018

For several decades, attribute classification methods using the asymmetrical relationship between an attribute performance and the satisfaction of that attribute have been explored by numerous researchers. In particular, the Kano model, which classifies quality attributes into 5 elements using simple questionnaire and two-dimensional evaluation table, has gained popularity: Attractive, One-dimensional, Must-be, Indifferent, and Reverse quality. As Kano's model is well accepted, many literatures have introduced categorization methods using the Kano's evaluation table at attribute level. However, they applied different terminologies and classification criteria and this causes confusion and misunderstanding. Therefore, a criterion for quality classification at attribute level is necessary. This study is aimed to suggest a new attribute classification method that sub-categorizes quality attributes using 5-point ordinal point and Kano's two-dimensional evaluation table through an extensive literature review. For this, the current study examines the intrinsic and extrinsic problems of the well-recognized Kano model that have been used for measuring customer satisfaction of products and services. For empirical study, the author conducted a comparative study between the results of Kano's model and the proposed method for an e-learning case (33 attributes). Results show that the proposed method is better in terms of ease of use and understanding of kano's results and this result will contribute to the further development of the attractive quality theory that enables to understand both the customers explicit and implicit needs.

• Journal of Information Processing Systems
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• 제1권1호
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• pp.79-85
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• 2005

In this paper, we dealt with substantial asset analysis methodology applied to two-dimensional asset classification and qualitative evaluation method according to the business process. Most of the existent risk analysis methodology and tools presented classification by asset type and physical evaluation by a quantitative method. We focused our research on qualitative evaluation with 2-dimensional asset classification. It converts from quantitative asset value with purchase cost, recovery and exchange cost, etc. to qualitative evaluation considering specific factors related to the business process. In the first phase, we classified the IT assets into tangible and intangible assets, including human and information data asset, and evaluated their value. Then, we converted the quantitative asset value to the qualitative asset value using a conversion standard table. In the second phase, we reclassified the assets using 2-dimensional classification factors reflecting the business process, and applied weight to the first evaluation results. This method is to consider the organization characteristics, IT asset structure scheme and business process. Therefore, we can evaluate the concrete and substantial asset value corresponding to the organization business process, even if they are the same asset type.

• The Journal of the Acoustical Society of Korea
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• 제23권3E호
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• pp.91-97
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• 2004

In this paper, we propose a classification system of TV program scenes based on audio information. The system classifies the video scene into six categories of commercials, basketball games, football games, news reports, weather forecasts and music videos. Two type of audio feature set are extracted from each audio frame-timbral features and coefficient domain features which result in 58-dimensional feature vector. In order to reduce the computational complexity of the system, 58-dimensional feature set is further optimized to yield l0-dimensional features through Sequential Forward Selection (SFS) method. This down-sized feature set is finally used to train and classify the given TV program scenes using κ -NN, Gaussian pattern matching algorithm. The classification result of 91.6% reported here shows the promising performance of the video scene classification based on the audio information. Finally, the system stability problem corresponding to different query length is investigated.

• 한국정보과학회논문지:정보통신
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• 제32권4호
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• pp.543-550
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• 2005

인터넷은 그 급속한 성장과 더불어 점차 더 나은 서비스를 제공할 것을 요구받게 되었다. 이에 따라 차세대 인터넷 라우터들에서의 지능적인 패킷 분류 기능은 필수 불가결한 것으로 여겨지고 있다. 패킷 분류란 미리 정의된 classifier에 의거하여 입력된 패킷에 매치하는 가장 순위가 높은 룰을 찾는 과정이다. 기존에 나와있는 많은 패킷 분류 검색 구조들이 출발지, 목적지 프리픽스 필드에 기반하여 룰을 추려내는 접근 방법을 사용하고 있다. 그러나 대부분의 검색 구조들은 출발지, 목적지 프리픽스 검색을 위하여 트라이 구조에 바탕을 둔 순차적인 일차원 검색을 따르고 있으며, 매우 큰 메모리를 요구한다는 단점을 가지고 있다. 본 논문에서는 메모리를 매우 효율적으로 사용하면서도 출발지-목적지 프리픽스 쌍에 기반한 이차원 패킷 분류 구조를 제안하고자 한다. 코드워드로 구성된 이진 프리픽스 트리를 구성함으로써, 출발지 프리픽스 검색과 목적지 프리픽스 검색이 하나의 이진 트리를 통해 동시에 가능하도록 하였다. 또한 본 논문에서 제안하는 구조인 이차원 이진 프리픽스 트리는 트리 구조 내부에 비어있는 노드를 포함하고 있지 않으므로 트라이 구조가 가지고 있는 메모리의 비효율성 문제를 완전히 제거하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권11호
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• pp.4246-4267
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• 2020

As the network goes deep into all aspects of people's lives, the number and the complexity of network traffic is increasing, and traffic classification becomes more and more important. How to classify them effectively is an important prerequisite for network management and planning, and ensuring network security. With the continuous development of deep learning, more and more traffic classification begins to use it as the main method, which achieves better results than traditional classification methods. In this paper, we provide a comprehensive review of network traffic classification based on deep learning. Firstly, we introduce the research background and progress of network traffic classification. Then, we summarize and compare traffic classification based on deep learning such as stack autoencoder, one-dimensional convolution neural network, two-dimensional convolution neural network, three-dimensional convolution neural network, long short-term memory network and Deep Belief Networks. In addition, we compare traffic classification based on deep learning with other methods such as based on port number, deep packets detection and machine learning. Finally, the future research directions of network traffic classification based on deep learning are prospected.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권2호
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• pp.420-437
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• 2024

There are some problems in network traffic classification (NTC), such as complicated statistical features and insufficient training samples, which may cause poor classification effect. A NTC architecture based on one-dimensional Convolutional Neural Network (CNN) and transfer learning is proposed to tackle these problems and improve the fine-grained classification performance. The key points of the proposed architecture include: (1) Model classification--by extracting normalized rate feature set from original data, plus existing statistical features to optimize the CNN NTC model. (2) To apply transfer learning in the classification to improve NTC performance. We collect two typical network flows data from Youku and YouTube, and verify the proposed method through extensive experiments. The results show that compared with existing methods, our method could improve the classification accuracy by around 3-5%for Youku, and by about 7 to 27% for YouTube.

• 한국신뢰성학회지:신뢰성응용연구
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• 제10권3호
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• pp.183-197
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• 2010

Web businesses are one of the most dynamic industries where lots of new business models are emerging while the other obsoleted ones are fading away almost every day. It is, therefore, difficult to establish a classification scheme for ever-changing web businesses. Previous researches on business models focus on classifying web businesses in one dimension which made some web sites difficult to fit into one category. We propose two dimensional classification scheme based on the means and the sources of revenue. The two dimensional classification provides more clear and broad perspectives of the web businesses and ways to identify web sites in combinations of several business models.

• 응용통계연구
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• 제30권5호
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• pp.793-808
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• 2017

본 논문에서는 초고차원 자료의 다항분류를 위한 변수선별 방법에 대해 비교 연구를 진행하였다. 다항분류를 위한 변수선별 방법에는 일대일 혹은 일대다 비교를 통해 이항분류를 위한 방법을 확장시켜 적용하는 방법과 다항 반응 변수에 직접 적용할 수 있는 방법이 있다. 다항분류를 위한 변수선별 성능을 확인하기 위하여 여러가지 상황-설명변수의 꼬리가 두꺼운 경우, 신호변수와 잡음변수가 서로 연관된 경우, 결합분포상으로 연관되어 있지만 주변분포 상으로는 연관되어 있지 않은 경우, 다범주 반응변수의 분포가 불균형인 경우-을 가정하고 모의실험을 진행하였고, 실제 자료에도 적용해 보았다. 그 결과, 모형 가정을 필요로 하지 않는 방법들이 안정적인 성능을 보이는 것을 확인하였다.

• 한국잡초학회지
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• 제2권2호
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• pp.129-140
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• 1982

식물(植物) 생태학(生態學)에서 식생분석(植生分析)에 이용(利用)되는 two-dimensional ordination 분석법(分析法)을 사용(使用)하여 잡초군락형살초(雜草群落型殺草) spectrum을 분류(分類)하여 효과적(效果的)인 제초제선발(除草制選拔), 체계처리(體系處理) 및 약제가격절감(藥劑價格節減)을 위(爲)한 기초자료(基礎資料)를 얻고자 2개년(個年)(1980, 1981)에 걸쳐 포장시험(圃場試驗) 및 pot시험(試驗)으로 실시(實施)하였던 결과(結果)를 요약(要約)하면 다음과 같다. 1. 26종(種)의 수도용(水稻用) 제초제(除草劑)를 pot 시험(試驗)으로 처리(處理)하여 제초제군(除草劑群)을 분류(分類)한 결과(結果) 마디꽃 (48%) - 올챙고랭이 (35%) - 알방동산이 (8%) 잡초군락형(雜草群落型)에서는 6개군(個群), 마디꽃 (45%) - 물달개비 (35%) - 바람하늘직이 (9%) 잡초군락형(雜草群落型)에서는 10 개군(個群)으로 분류(分類)되었다. 2. 포장(圃場) 시험결과(試驗結果)에 있어 18종(種)의 제초제(除草劑)를 너도방동산이 (37%) - 올챙고랭이 (19%) - 물달개비 (14%) - 피 (7%) - 사마귀풀 (6%) 잡초군락형((雜草群落型)에서 처리(處理)하였던 결과(結果) 7개(個) 제초제군(除草劑郡)으로 분류(分類)되었고, 물달개비 (40%) - 여뀌바늘 (27%) - 너도 방동산이 (17%) - 올챙고랭이 (12%) 잡초군락형(雜草群落型에)에서 19종(種)의 제초제(除草劑)를 처리(處理)하였던 결과(結果)는 14개(個) 제초제군(除草劑群)으로 분류(分類)되었다. 3. two-dimensional ordination 분석법(分析法)은 제초제살초(除草劑殺草) spectrum 분류(分類)뿐만 아니라 제초제처리방법(除草劑處理方法)에 따른 문제잡초(問題雜草)를 구명(究明)하는데도 이용(利用)이 가능(可能)하였다. 4. 본(本) 시험결과(試驗結果)를 미루어 보아 효과적(效果的)인 제초제사용(除草劑使用)을 위(爲)해서는 단순(單純)히 제초제(除草劑) 상호간(相互間)의 유사성계수(類似性係數)(Similarity coefficient)를 이용(利用)하는 것 보다 two-dimensional ordination 분석법(分析法)을 이용(利用)하므로서 효과적(效果的)인 제초제선발(除草劑選拔), 살초(殺草) spectrum 증대(增大)를 위(爲)한 혼합처리(混合處理) 또는 체계처리(體系處理) 및 약제가격(藥劑價格)을 절감(節減)하기 위(爲)한 혼합처리(混合處理) 등(等)에 관(關)한 정보(情報)를 비교적(比較的) 쉽게 얻을 수 있었다.