• 대한산업공학회지
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• 제33권2호
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• pp.183-190
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• 2007

We present a new decision tree classification algorithm using rough set theory that can induce classification rules, the construction of which is based on core attributes and relationship between objects. Although decision trees have been widely used in machine learning and artificial intelligence, little research has focused on improving classification quality. We propose a new decision tree construction algorithm that can be simplified and provides an improved classification quality. We also compare the new algorithm with the ID3 algorithm in terms of the number of rules.

• 산업경영시스템학회지
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• 제29권2호
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• pp.13-19
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• 2006

The instance-based learning is a machine learning technique that has proven to be successful over a wide range of classification problems. Despite its high classification accuracy, however, it has a relatively high storage requirement and because it must search through all instances to classify unseen cases, it is slow to perform classification. In this paper, we have presented a new data reduction method for instance-based learning that integrates the strength of instance partitioning and attribute selection. Experimental results show that reducing the amount of data for instance-based learning reduces data storage requirements, lowers computational costs, minimizes noise, and can facilitates a more rapid search.

• 대한산업공학회지
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• 제40권4호
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• pp.375-381
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• 2014

Data mining is a process of discovering useful patterns or information from large amount of data. Decision tree is one of the data mining algorithms that can be used for both classification and prediction and has been widely used for various applications because of its flexibility and interpretability. Decision trees for classification generally generate a number of rules that belong to one of the predefined category and some rules may belong to the same category. In this case, it is necessary to determine the significance of each rule so as to provide the priority of the rule with users. The purpose of this paper is to propose a rule selection method in classification tree models that accommodate the umber of observation, accuracy, and effectiveness in each rule. Our experiments demonstrate that the proposed method produce better performance compared to other existing rule selection methods.

• Industrial Engineering and Management Systems
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• 제13권4호
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• pp.449-453
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• 2014

Chronic diseases including hypertension and its complications are major sources causing the national medical expenditures to increase. We aim to predict the risk of hypertension complications for hypertension patients, using the sample national healthcare database established by Korean National Health Insurance Corporation. We apply classification techniques, such as logistic regression, linear discriminant analysis, and classification and regression tree to predict the hypertension complication onset event for each patient. The performance of these three methods is compared in terms of accuracy, sensitivity and specificity. The result shows that these methods seem to perform similarly although the logistic regression performs marginally better than the others.

• 한국산학기술학회논문지
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• 제9권6호
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• pp.1889-1893
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• 2008

본 논문은 영상 분류 문제를 위한 support vector machines (SVMs)의 적용을 통한 분류의 성능을 다루고 있다. 본 연구에서는 영상 분류 문제에서 자연영상을 대상으로 색상, 질감, 형상 특징벡터를 추출하고, 각각의 특징벡터와 이들을 결합한 특징벡터를 사용하여 역전파 신경망과 SVM 기반의 방법을 적용하여 영상 분류의 정확성을 비교한다. 실험결과는 각각의 특징벡터중에는 색상 특징벡터값을 이용한 영상 분류가 그리고 각각의 특징벡터보다는 이들을 결합한 특징벡터를 이용한 영상 분류가 보다 우수함을 보여준다. 그리고 알고리즘간의 비교에서는 정확성과 일반화성능 측면에서 역전파 신경망보다 SVMs이 우수함을 보였다.

• 대한산업공학회지
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• 제28권2호
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• pp.223-231
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• 2002

Recently, work-related musculoskeletal disorders(WMSDs) have rapidly increased and have been a major issue in the field of industrial safety. Of several physical risk factors for WMSDs, which include postures, vibration, repetitive work, speed or acceleration of movements, etc., awkward postures have been known as one of the major causes of WMSDs. For reducing the potential for injury as a result of postures, cost effective quantification of the magnitude for physical exposure to poor working postures is important and needed. To do this, several postural classification schemes have been developed and used in industrial sites. It is known that perceived discomfort for joint motions and muscle strength for females were much less than those for males. However, the existing postural classification schemes were developed without considering these gender effects. This study aims to develop a new postural classification scheme for female workers, based on the perceived discomfort for joint motions. The result showed that there was significant difference between the schemes for female and male. It was also found that when compared with OWAS, RULA and REBA, postural load was quantified more precisely with the developed scheme. It is recommended that different schemes according to gender of workers involved in work be used in order to accurately evaluate postural load of work postures.

• 디자인학연구
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• 제17권3호
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• pp.71-80
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• 2004

문화산업디자인은 미래 산업의 선진화를 이룩할 수 있는 핵심적인 계기와 수단은 물론, 다양한 역량으로 발전되어 새로운 가치변화를 촉진시키며 기업과 국가의 경쟁력을 극대화시키는 주요 요인이다. 따라서 이에 대한 새로운 패러다임 변화를 이해하고 구조적 특성을 재 조망하는 것은 매우 중요한 의미를 지닌다. 그러므로 문화산업디자인 정책 및 전략수립, 진단지표의 체계구성과 평가에 필요한 객관적인 자료를 확보하기 위해서는 기본이 되는 분류체계 구축이 선행적으로 이뤄져야 한다. 본 연구의 문화산업디자인 분류체계(CIDC)는 크게 3영역으로 구분하였고 각각 중, 소, 세 분류의 계층적 구조와 레이어 기호로 표기하였다. CIDC는 이 분야의 기초적인 언더데이터로서의 역할을 담당할 뿐만 아니라 연관 디자인 분야 분류체계와 비교를 통한 차별성과 연계성을 검색할 수 있도록 구성하였다.

• 대한산업공학회지
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• 제36권1호
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• pp.13-21
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• 2010

The prediction of contract cancellation of customers is essential in insurance companies but it is a difficult problem because the customer database is large and the target or cancelled customers are a small proportion of the database. This paper proposes a new data mining approach to the binary classification by handling a large-scale unbalanced data. Over-sampling, clustering, regularized logistic regression and boosting are also incorporated in the proposed approach. The proposed approach was applied to a real data set in the area of insurance and the results were compared with some other classification techniques.

• Industrial Engineering and Management Systems
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• 제2권1호
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• pp.35-44
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• 2003

Recovery of end-of-life (EOL) products is an environmentally and economically sound way to achieve many of the goals of sustainable development. Many product recovery systems are dependent upon destructive disassembly such as shredding, which undesirably causes a large volume of shredder dust and makes parts reuse impossible. Although non-destructive disassembly has been considered as an alternative for solving the problems, the classification of disassembled items has not been sufficiently investigated. In this paper, we propose a model that mathematically optimizes the disassembly and classification of EOL products. Based on the AND/OR graph that illustrates all possible disassembly sequences of a given product, we identify the physical properties that are considered as constraints in the model. As a result of the solution procedure, the recovery problem can be transformed into a mixed integer linear programming (MILP) model. We show an example that illustrates the concept of our model.

• Journal of the Korean Data and Information Science Society
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• 제22권5호
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• pp.931-940
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• 2011

분류분석은 학습표본으로부터 분류규칙을 도출한 후 새로운 표본에 적용하여 특정 범주로 분류하는 방법이다. 데이터의 복잡성에 따라 다양한 분류분석 방법이 개발되어 왔지만, 데이터 차원이 높고 변수간 상관성이 높은 경우 정확하게 분류하는 것은 쉽지 않다. 본 연구에서는 데이터차원이 상대적으로 높고 변수간 상관성이 높을 때 강건한 분류방법을 제안하고자 한다. 부분최소자승법은 연속형데이터에 사용되는 기법으로서 고차원이면서 독립변수간 상관성이 높을 때 예측력이 높은 통계기법으로 알려져 있는 다변량 분석기법이다. 벌점 부분최소자승법을 이용한 분류방법을 실제데이터와 시뮬레이션을 적용하여 성능을 비교하고자 한다.