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대부분의 기계학습 알고리즘은 학습 데이터에서 각각의 범주간의 비율이 동일하거나 비슷하다는 가정 하에 문제를 풀게 된다. 그러나 실제 문제에서는 그 비율이 동일하지 않으며 매우 큰 차이를 보이기도 하는데 이는 분류 성능을 저하시키는 요인이기도 하다. 따라서 본 논문에서는 이러한 데이터의 불균형 문제를 해소하는 방안으로 SVM 앙상블 기법을 적용한 샘플링을 제안하고 이를 실제 불균형 데이터에 적용함으로써 제안된 방법이 기존의 방법들에 비해 향상된 성능을 나타내는 것을 보였다.
This paper proposes a new approach to technology valuation, the market-replacement cost approach which integrates the cost-based approach and market-based approach. The proposed approach estimates the market-replacement cost of a target technology using R&D costs of similar R&D projects previously conducted. Similar R&D projects are extracted from project database based on document similarity between project proposals and technology description of the target technology. R&D costs of similar R&D projects are adjusted by mirroring the rate of technological obsolescence and inflation. Market-replacement cost of the technology is then derived by calculating the weighted average of adjusted costs and similarity values of similar R&D projects. A case of “Prevention method and system for the diffusion of mobile malicious code” is presented to illustrate the proposed approach.
The purpose of virtual metrology (VM) in semiconductor manufacturing is to predict every wafer’s metrological values based on its process equipment data without an actual metrology. In this paper, we propose novelty detection-based reliability estimation models for VM in order to support flexible utilization of VM results. Because the proposed model can not only estimate the reliability of VM, but also identify suspicious process variables lowering the reliability, quality control actions can be taken selectively based on the reliance level and its causes. Based on the preliminary experimental results with actual semiconductor manufacturing process data, our models can successfully give a high reliance level to the wafers with small prediction errors and a low reliance level to the wafers with large prediction errors. In addition, our proposed model can give more detailed information by identifying the critical process variables and their relative impacts on the low reliability.
This paper aims to propose a research framework of analyzing voting activities of a national assembly on the basis of member-level voting similarity and provides a case study in the 18<SUP>th</SUP> national assembly in South Korea. First, we propose a bill contentiousness measure that gives a higher score to bills for which ayes and noes are more diversified in both conservative and progressive parties. Based on the bill contentiousness measure, the top 5%, 10%, and 20% bills were identified and used for further analyses. Moreover, we propose a member-level voting similarity measure that compensates for the lower frequency of noes, and evaluate the pair-wise voting similarities for all lawmakers. Then, voting similarity differences to the affiliated/non-affiliated parties were analyzed for the members in the two major parties according to some internal/external key factors. Finally, similar voting groups were identified and their affiliations were investigated based on the multi-dimensional scaling (MDS) and network analysis techniques. A case study on the 18<SUP>th</SUP> national assembly of South Korea showed that the cohesion of the members in the ‘Hanara’ party becomes higher than that of the ‘Minju’ party as the bill contentiousness increases, whereas the number of elected, local constituency versus proportional representation, and the competition intensity in a local constituency were found to be partially influential to the voting activities of lawmakers. In addition, MDS and network analysis showed that there is a distinctive difference between two parties when all bills are analyzed, whereas the diversity of parties increases in the same group as the bill contentiousness increases.
Classification algorithms generally assume that the data is complete. However, missing values are common in real data sets due to various reasons. In this paper, we propose to use locally linear reconstruction (LLR) for missing value imputation to improve the classification performance when missing values exist. We first investigate how much missing values degenerate the classification performance with regard to various missing ratios. Then, we compare the proposed missing value imputation (LLR) with three well-known single imputation methods over three different classifiers using eight data sets. The experimental results showed that (1) any imputation methods, although some of them are very simple, helped to improve the classification accuracy; (2) among the imputation methods, the proposed LLR imputation was the most effective over all missing ratios, and (3) when the missing ratio is relatively high, LLR was outstanding and its classification accuracy was as high as the classification accuracy derived from the compete data set.
본 논문에서는 품질기능전개(Quality Function Deployment, QFD) 기법을 이용하여 인지재활을 위한 ICT 서비스 항목을 구성한다. 첫째로, 일반적인 의료 서비스 관련 품질 평가 기준을 수립 후, 서비스 제공자인 치료사 및 서비스 수요자인 환자 사이에서 운영되는 현행 인지재활 서비스에 대한 설문조사를 실시하였다. 두 서비스 이용자 간 요구사항에 대한 가중치를 도출하기 위해 설문 응답 결과를 분석하였으며, 의료 분야 ICT 전문가 검토를 통하여 서비스 이용자 요구사항과 연계된 서비스 구성 항목을 선정하였다. 최종적으로 품질기능 전개 절차에 따라 구축된 품질의 집 (House of Quality, HoQ) 를 통하여 ICT 서비스 항목의 우선순위가 결정되었다. 그 결과, 가장 중요한 서비스 속성으로 ‘이동성’이 발견되었으며 ‘지속관찰’ 및 ‘조기경보’ 항목이 뒤를 이었다. This paper configures information and communication technology (ICT) service attributes for cognitive rehabilitation based on quality function deployment (QFD). Initially, quality assessment criteria for general medical service are identified. Then, a survey on the current cognitive rehabilitation service is conducted for both therapists as service providers and patients as customers. Their responses are analyzed to assign the weight of each customer requirement. Then, service components are identified and the relationship between the customer requirements and service components are mapped through thorough interviews with ICT experts in medical service industry. Finally, the house of quality is built through the process of quality function deployment and the priority of ICT service components are determined. ‘Mobile’ is found to be the most important attributes, followed by ‘Monitoring’ and ‘Early warning’.
User authentication is an important issue on computer network systems. Most of the current computer network systems use the ID-password string match as the primary user authentication method. However, in password-based authentication, whoever acquires the password of a valid user can access the system without any restrictions. In this paper, we present a keystroke dynamics-based user authentication to resolve limitations of the password-based authentication. Since most previous studies employed a fixed-length text as an input data, we aims at enhancing the authentication performance by combining four different variable creation methods from a variable-length free text as an input data. As authentication algorithms, four one-class classifiers are employed. We verify the proposed approach through an experiment based on actual keystroke data collected from 100 participants who provided more than 17,000 keystrokes for both Korean and English. The experimental results show that our proposed method significantly improve the authentication performance compared to the existing approaches.