• The KIPS Transactions:PartD
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• v.8D no.3
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• pp.203-210
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• 2001

These days data is being collected and accumulated in a wide variety of fields. Stored data itself is to be an information system which helps us to make decisions. An information system includes many kinds of necessary and unnecessary attribute. So many algorithms have been developed for finding useful patterns from the data and reasoning approximately new objects. We are interested in the simple and understandable rules that can represent useful patterns. In this paper we propose an algorithm which can reduce the information in the system to a minimum, based on a probabilistic rough set theory. The proposed algorithm uses a value that tolerates accuracy of classification. The tolerant value helps minimizing the necessary attribute which is needed to reason a new object by reducing conditional attributes. It has the advantage that it reduces the time of generalizing rules. We experiment a proposed algorithm with the IRIS data and Wisconsin Breast Cancer data. The experiment results show that this algorithm retrieves a small reduct, and minimizes the size of the rule under the tolerant classification rate.

• The Journal of Information Systems
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• v.22 no.2
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• pp.105-129
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• 2013

Recently, since the interest with well-being has been getting higher than ever, people want reliable source of information related with health and medical treatment. Because of the characteristics of information related with medical care, there have been difficulties to find the information from books, television and internet surfing, for treating disease. Misinformation that can be obtained when considering dangerous situations or side effects, the role of the e-Health system is becoming more important. The objective of this study is an analysis of correlation and effect among patient's information need, e-Health system use and system outcome. To achieve the object of this study, e-Health system had been given to patients of breast cancer in Wisconsin and Detroit for 16 weeks. As a result, 282 sample was gathered and modified to meet purpose of the study. As a result, the information needs of patients due to the performance of the e-Health systems and shown to affect even the perception of patients' emotional and physical health and social support.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.1
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• pp.20-25
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• 2009

Mahalanobis Taguchi System (MTS) is a pattern information technology, which has been used in different diagnostic applications to make quantitative decisions by constructing a multivariate measurement scale using data analytic methods without any assumption regarding statistical distribution. The MTS performs Taguchi's fractional factorial design based on the Mahahlanobis Distance (MS) as a performance metric. In this work, MTS is used for analyzing Wisconsin Breast Cancer data which has ten attributes. Ten different tests are conducted for the data to determine if the patient has cancer or not. Also, MTS is used for reducing the number of test to define the relationship between each attribute and diagnosis result. The accuracy of diagnosis is compare with two different previous research.

• Journal of Digital Convergence
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• v.19 no.12
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• pp.347-352
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• 2021

In this paper, we propose a method for finding feature subsets that are effective for classification in an input dataset by using a multi-agent reinforcement learning method. In the field of machine learning, it is crucial to find features suitable for classification. A dataset may have numerous features; while some features may be effective for classification or prediction, others may have little or rather negative effects on results. In machine learning problems, feature selection for increasing classification or prediction accuracy is a critical problem. To solve this problem, we proposed a feature selection method based on reinforced learning. Each feature has one agent, which determines whether the feature is selected. After obtaining corresponding rewards for each feature that is selected, but not by the agents, the Q-value of each agent is updated by comparing the rewards. The reward comparison of the two subsets helps agents determine whether their actions were right. These processes are performed as many times as the number of episodes, and finally, features are selected. As a result of applying this method to the Wisconsin Breast Cancer, Spambase, Musk, and Colon Cancer datasets, accuracy improvements of 0.0385, 0.0904, 0.1252 and 0.2055 were shown, respectively, and finally, classification accuracies of 0.9789, 0.9311, 0.9691 and 0.9474 were achieved, respectively. It was proved that our proposed method could properly select features that were effective for classification and increase classification accuracy.

• Proceedings of the Safety Management and Science Conference
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• 2005.11a
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• pp.300-310
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• 2005

Mahalanobis Taguchi System (MTS) is developed by Genishi Taguchi as a part of his quality engineering methodology. The basic idea of Taguchi's quality engineering is looking for the way of effectiveness of analyzing multivariate system. In the MTS, with the standardized variables of healthy normal data, Mahalanobis Distance(MD) calculated and that can be discriminate between normal and abnormal objects. If this discrimination process is successful, next step is optimization which is try to reduce number of attributes by neglecting less effective attributes to MD. Orthogonal Array (OA) and Signal to Noise ratio (S/N) are used to evaluate the amount contribution of each attribute to the MD. Wisconsin Breast Cancer study, from machining learning repository at University of California at Irvine, used for examining the discriminant ability of MTS.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.462-465
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• 2004

수치적인 데이터를 분류하기 위한 대표적인 방법은 퍼지 규칙을 사용하는 것이다. 하지만, 이러한 방법은 퍼지 소속 함수를 어떻게 정의하느냐에 따라 퍼지 분류의 성능이 크게 영향을 받는다는 문제점과 퍼지 규칙을 쉽게 이해하기 위해 가능한 퍼지 규칙의 수를 적게 유지해야한다는 문제점이 있다. 본 논문에서는 효과적이며 이해하기 쉬운 퍼지 규칙을 생성하기 위해 기울기 강하법을 기반으로 하는 소속 함수 학습 방법을 제안한다. 에러율을 감소하기 위해 Penalty 연산과 Reward 연산을 통해 소속 함수가 반복적으로 조절된다. 새로운 소속 함수는 Coverage 연산에 의해 생성된다. 또한 이해하기 쉬운 퍼지 규칙을 최적화하기 위해 학습된 소속 함수를 퍼지 결정 트리에 적용한다. 본 논문에서 제안한 알고리즘의 타당성을 확인하기 위해 벤치 마크 데이터인 Iris, Wisconsin Breast Cancer, Pima. Bupa 데이터를 이용하여 실험 결과를 보인다. 실험 결과를 통해 제안한 알고리즘이 기존의 C4.5와 FID 3.1 알고리즘보다 더 효과적이거나 비슷한 성능을 보임을 알 수 있다.

• Journal of the Korean Data and Information Science Society
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• v.16 no.4
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• pp.1017-1026
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• 2005

We consider the problem of modeling count data where the observation period is determined by the survival time of the individual under study. We assume random effects or frailty model to allow for a possible association between the death times and the counts. We assume that, given a random effect, the death times follow a Weibull distribution with a rate that depends on some covariates. For the counts, given the random effect, a Poisson process is assumed with the intensity depending on time and the covariates. A gamma model is assumed for the random effect. Maximum likelihood estimators of the model parameters are obtained. The model is applied to data set of patients with breast cancer who received a bone marrow transplant. A model for the time to death and the number of supportive transfusions a patient received is constructed and consequences of the model are examined.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.613-615
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• 2004

수치적인 데이터를 분류하기 위한 대표적인 방법은 퍼지 규칙을 사용하는 것이다. 하지만 퍼지 규칙을 이용하는 방법은 퍼지 소속 함수를 어떻게 정의하느냐에 따라 퍼지 분류의 성능이 크게 영향을 받는다는 문제점이 있다. 따라서 퍼지 규칙을 쉽게 이해하기 위해서는 가능한 퍼지 규칙의 수를 적게 유지하는 것이 필요하다. 본 논문에서는 효과적이며 이해하기 쉬운 퍼지 규칙을 생성하기 위해 기울기 강하법을 기반으로 하는 소속 함수 학습 방법을 제안한다 에러율을 감소하기 위해 Penalty 연산과 Reward 연산을 통해 소속 함수가 반복적으로 조절된다 새로운 소속 함수는 Coverage 연산에 의해 생성된다. 또한 이해하기 쉬운 퍼지 규칙을 최적화하기 위해 학습된 소속 함수골 퍼지 결정 트리에 적용한다. 본 논문에서 제안한 알고리즘의 타당성을 확인하기 위해 벤치 마크 데이터인 Iris, Wisconsin Breast Cancer, Plma, Bupa 데이터를 이용하여 실험 결과를 보인다. 실험 결과를 통해 제안한 알고리즘이 기존의 C4.5와 FID 3.1 알고리즘보다 더 효과적이거나 비슷한 성능을 보임을 알 수 있다.

• Asian Oncology Nursing
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• v.12 no.4
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• pp.267-273
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• 2012

Purpose: The purpose of this review article is to introduce how the Korean Society of Genetic Nursing (KSGN) has evolved and tried to translate genomic knowledge to nursing practice, and then to suggest the future role of genetic nurses in Korea. Methods: A literature review was performed and the current status of genetic counselling in Korea was explored. Then the educational and clinical experiences of the authors were incorporated. Finally, the main activities of Korean nursing for genetics were identified. Results: Two types of genetic counsellor certification have been issued in Korea: one is issued by the Korean Society of Genetic Medicine, another by the Korean Society of Breast Cancer since June 2011. A few Korean nursing researchers have continuously performed research related to genetic nursing and undertook several research projects funded by the government since 2003. In February 2011, KSGN was established and is now trying to establish further international networks. Conclusion: Nursing genetic experts should be trained to integrate all specialties for genetic counselling, so they can provide holistic genetic services including ethical, legal, and social issues (ELSI).

• Journal of KIISE:Software and Applications
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• v.26 no.11
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• pp.1314-1323
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• 1999

컴퓨터의 사용이 일반화됨에 따라 데이타를 생성하고 수집하는 것이 용이해졌다. 이에 따라 데이타로부터 자동적으로 유용한 지식을 얻는 기술이 필요하게 되었다. 데이타 마이닝에서 얻어진 지식은 정확성과 이해성을 충족해야 한다. 본 논문에서는 데이타 마이닝을 위하여 퍼지 결정트리에 기반한 효율적인 퍼지 규칙을 생성하는 알고리즘을 제안한다. 퍼지 결정트리는 ID3와 C4.5의 이해성과 퍼지이론의 추론과 표현력을 결합한 방법이다. 특히, 퍼지 규칙은 속성 축에 평행하게 판단 경계선을 결정하는 방법으로는 어려운 속성 축에 평행하지 않는 경계선을 갖는 패턴을 효율적으로 분류한다. 제안된 알고리즘은 첫째, 각 속성 데이타의 히스토그램 분석을 통해 적절한 소속함수를 생성한다. 둘째, 주어진 소속함수를 바탕으로 ID3와 C4.5와 유사한 방법으로 퍼지 결정트리를 생성한다. 또한, 유전자 알고리즘을 이용하여 소속함수를 조율한다. IRIS 데이타, Wisconsin breast cancer 데이타, credit screening 데이타 등 벤치마크 데이타들에 대한 실험 결과 제안된 방법이 C4.5 방법을 포함한 다른 방법보다 성능과 규칙의 이해성에서 보다 효율적임을 보인다.Abstract With an extended use of computers, we can easily generate and collect data. There is a need to acquire useful knowledge from data automatically. In data mining the acquired knowledge needs to be both accurate and comprehensible. In this paper, we propose an efficient fuzzy rule generation algorithm based on fuzzy decision tree for data mining. We combine the comprehensibility of rules generated based on decision tree such as ID3 and C4.5 and the expressive power of fuzzy sets. Particularly, fuzzy rules allow us to effectively classify patterns of non-axis-parallel decision boundaries, which are difficult to do using attribute-based classification methods.In our algorithm we first determine an appropriate set of membership functions for each attribute of data using histogram analysis. Given a set of membership functions then we construct a fuzzy decision tree in a similar way to that of ID3 and C4.5. We also apply genetic algorithm to tune the initial set of membership functions. We have experimented our algorithm with several benchmark data sets including the IRIS data, the Wisconsin breast cancer data, and the credit screening data. The experiment results show that our method is more efficient in performance and comprehensibility of rules compared with other methods including C4.5.