• Journal of KIISE
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• v.43 no.12
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• pp.1334-1341
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• 2016

In recent years, the importance of managing software defects in the implementation stage has emerged because of the rapid development and wide-range usage of intelligent smart devices. Even if not a few studies have been conducted on the prediction models for software defects, their outcomes have not been widely shared. This paper proposes an efficient probabilistic management model of software metrics based on the Bayesian network, to overcome limits such as binary defect prediction models. We expect the proposed model to configure the Bayesian network by taking advantage of various software metrics, which can help in identifying improvements for refactoring. Once the source code has improved through code refactoring, the measured related metric values will also change. The proposed model presents probability values reflecting the effects after defect removal, which can be achieved by improving metrics through refactoring. This model could cope with the conclusive binary predictions, and consequently secure flexibilities on decision making, using indeterminate probability values.

• Knowledge Management Research
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• v.10 no.4
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• pp.1-15
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• 2009

국내에서 신용카드는 대표적인 지불 수단으로 정착되었으며 신용카드의 사용자와 신용카드의 발급 매수는 이미 포화상태에 도달해 있다. 이 같은 양적 성장은 정부의 신용카드 활성화 정책과 더불어 신용카드사 간의 과당 경쟁의 영향에 기인하고 있다. 신용차드의 사용층은 대부분의 성인 남녀로 확대되었으며, 특히 복수의 신용카드 소지자를 대상으로 자사가 발급한 신용차드를 사용하게 하기 위한 신용카드사 간의 경쟁이 치열한 상황이다. 이에 따라 신용카드사들이 경쟁사의 카드사용 회원을 자사의 회원으로 확보하는 젓이 불가피하며 마찬가지로 사용 중인 자사의 회원이 경쟁사로 이동하지 않도록 사전에 이탈 징후를 포착하여 유지 캠페인을 수행하는 것이 신용카드사 마케팅의 주요 활동이 되었다. 선행연구에서는 신용카드 회원의 이탈과 관련하여 다양한 데이터마이닝 기법을 이용한 이탈의 특성 분류 연구가 진행되었다. 본 연구는 회원 이탈에 영향을 주는 요인을 효과적으로 발견하기 위한 방법으로 베이지안 네트워크(Bayesian Network)를 활용한다. 특히, 베이지안 네트워크의 일종인 일반 베이지안 네트워크(General Bayesian Network)를 이용하여 회원의 이탈요인에 영향을 주는 요인들의 집합인 마코프 블랭킷(Makov Blanket)을 도출한다. 한편, 마코프 블랭킷에 포함된 변수를 이용해 민감도 분석을 수행하여 영향이 큰 요인을 찾아내고 이를 비즈니스 프로세스에 적용하여 실무적인 의의를 실증하고자 한다.

• Journal of Korea Multimedia Society
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• v.16 no.3
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• pp.269-280
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• 2013

In this paper, we suggested the possibility of using an efficient classifier for the dependency analysis of retinal disease. First, we analyzed the classification performance and the prediction accuracy of GBN (General Bayesian Network), GBN with reduced features by Markov Blanket and TAN (Tree-Augmented Naive Bayesian Network) among the various bayesian networks. And then, for the first time, we applied TAN showing high performance to the dependency analysis of the clinical data of retinal disease. As a result of this analysis, it showed applicability in the diagnosis and the prediction of prognosis of retinal disease.

• Journal of KIISE
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• v.44 no.11
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• pp.1149-1155
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• 2017

Recently, with the development of communication technology, it has become possible to collect various sensor data that indicate the environmental stimuli within a space. In this paper, we propose a group emotion prediction system using a modular Bayesian network that was designed considering the psychological impact of environmental stimuli. A Bayesian network can compensate for the uncertain and incomplete characteristics of the sensor data by the probabilistic consideration of the evidence for reasoning. Also, modularizing the Bayesian network has enabled flexible response and efficient reasoning of environmental stimulus fluctuations within the space. To verify the performance of the system, we predict public emotion based on the brightness, volume, temperature, humidity, color temperature, sound, smell, and group emotion data collected in a kindergarten. Experimental results show that the accuracy of the proposed method is 85% greater than that of other classification methods. Using quantitative and qualitative analyses, we explore the possibilities and limitations of probabilistic methodology for predicting group emotion.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.7
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• pp.817-822
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• 2005

Recently tile study that exploits visual information for tile services of robot in indoor environments is active. Conventional image processing approaches are based on the pre-defined geometric models, so their performances are likely to decrease when they are applied to the uncertain and dynamic environments. For this, diverse researches to manage the uncertainty based on the knowledge for improving image recognition performance have been doing. In this paper we propose a Bayesian network modeling method for predicting the existence of target objects when they are occluded by other ones for improving the object detection performance of the service robots. The proposed method makes object relationship, so that it allows to predict the target object through observed ones. For this, we define the design method for small size Bayesian networks (primitive Bayesian netqork), and allow to integrate them following to the situations. The experiments are performed for verifying the performance of constructed model, and they shows $82.8\%$ of accuracy in 5 places.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.3
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• pp.306-311
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• 2005

To analyze the consumer's purchase pattern, we must consider a factor which is a cultural, social, individual, psychological and so on. If we consider the internal state by the consumer's purchase, Both the consumer's purchase action and the purchase factor can be predicted, so the corporation can use effectively in suitable goods development in a consumer's preference. These factors need a technology that treat uncertain information, because it is difficult to analyze by directly information processing. Therefore, bayesian network manages elements those the observation of inner state such as consumer's purchase is difficult. In addition, it is interpretable about data that the observation is impossible. In this paper, we examine the seller's know-how and the way of consumer's purchase to analyze consumer's purchase action pattern through goods purchase. Also, we compose the bayesian network based on the examined data, and propose the method that predicts purchase patterns. Finally, we remove the data including unnecessary attribute using the bayesian network, and analyze the consumer's Purchase pattern using Kohonen's SOM method.

• Bioinformatics and Biosystems
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• v.1 no.1
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• pp.38-45
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• 2006

We present a client-server system architecture for inferring genetic interaction networks based on Bayesian networks. It is typical to take tens of hours when genome-wide large-scale genetic interaction networks are inferred in the form of Bayesian networks. To deal with this situation, batch-style distributed system architectures are preferable to interactive standalone architectures. Thus, we have implemented a loosely coupled client-server system for network inference and user interface. The network inference consists of two stages. Firstly, the proposed method divides a whole gene set into overlapped modules, based on biological annotations and expression data together. Secondly, it infers Bayesian networks for each module, and integrates the learned subnetworks to a global network through common genes across the modules.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.05a
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• pp.357-360
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• 2006

본 논문에서는 베이지안 네트워크 학습 방법을 이용한 비교적 적은 양의 샘플 데이터에서 현실적인 네트워크 모델 추론을 위한 효율적인 스코어링 척도를 찾는 것을 목표로 하였다. UPSM, CUPSM, DPSM, BDe(Bayesian Dirichlet) 등을 각각 적용시켜본 결과를 통해 어떤 방법이 가장 적은 샘플의 데이터, 특히 생물학적 데이터에적합한지 알아보았다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.01a
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• pp.71-72
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• 2016

베이지안 알고리즘은 추론 분야에서 오랜 기간 사용되어 왔다. 하지만 기본적인 베이지안 네트워크 이론만으로는 다양한 도메인에 적합한 추론 기능을 제공할 수 없기 때문에, 도메인의 특성에 맞는 알고리즘이 적용된 다양한 추론 기법들이 연구되어왔다. 본 논문에서는 실시간 지능화 서비스를 위하여 특정 도메인 영역에 대하여 자동으로 적합한 베이지안 네트워크 알고리즘을 선별하는 기법을 제안한며, 해당 기법의 적합도를 평가하기 위해서 수학적인 모델링과 추론 알고리즘 선택 기법에 대해 서술한다.

• Journal of Internet Computing and Services
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• v.6 no.1
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• pp.27-38
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• 2005

Recently, the rapidly development of computing environments and the spread of Internet make possible to obtain and use of information easily. Immediately, by opposition function the Hacker's unlawful intrusion and threats rise for network environments as time goes on. Specially, the internet consists of Unix and TCP/IP had many vulnerability. the security techniques of authentication and access controls cannot adequate to solve security problem, thus IDS developed with 2nd defence line. In this paper, intrusion detection method using Bayesian Networks estimated probability values of behavior contexts based on Bayes theory. The contexts of behaviors or events represents Bayesian Networks of graphic types. We profiled concisely normal behaviors using behavior context. And this method be able to detect new intrusions or modificated intrusions. We had simulation using DARPA 2000 Intrusion Data.