• IE interfaces
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• v.18 no.1
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• pp.10-21
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• 2005

With the increasing proliferation of World Wide Web, electronic mail systems have become very widely used communication tools. Researches on e-mail classification have been very important in that e-mail classification system is a major engine for e-mail response management systems which mine unstructured e-mail messages and automatically categorize them. In this research we compare the performance of Naive Bayesian learning and Centroid-Based Classification using the different data set of an on-line shopping mall and a credit card company. We analyze which method performs better under which conditions. We compared classification accuracy of them which depends on structure and size of train set and increasing numbers of class. The experimental results indicate that Naive Bayesian learning performs better, while Centroid-Based Classification is more robust in terms of classification accuracy.

• Journal of Computing Science and Engineering
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• v.1 no.2
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• pp.162-176
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• 2007

Machine Learning (ML) has become very popular within Data Mining (KDD) and Artificial Intelligence (AI) research and their applications. In the ML and KDD contexts, two main approaches can be used for inducing a Bayesian Network (BN) from data, namely, Conditional Independence (CI) and the Heuristic Search (HS). When a BN is induced for classification purposes (Bayesian Classifier - BC), it is possible to impose some specific constraints aiming at increasing the computational efficiency. In this paper a new CI based approach to induce BCs from data is proposed and two algorithms are presented. Such approach is based on the Markov Blanket concept in order to impose some constraints and optimize the traditional PC learning algorithm. Experiments performed with the ALARM, as well as other six UCI and three artificial domains revealed that the proposed approach tends to execute fewer comparison tests than the traditional PC. The experiments also show that the proposed algorithms produce competitive classification rates when compared with both, PC and Naive Bayes.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1313-1315
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• 2015

베이즈 분류기(Bayesian classifier)를 이용하여 특정 신문기사가 어떤 정치적 성향을 가지는지 분류한다. 이를 위하여 보수 및 진보 성향으로 알려진 언론사의 보도기사를 수집하여 베이즈 분류기를 학습하였다. 즉, 보수 및 진보적 성향을 갖는 기사에서 출현 빈도가 높은 단어의 빈도수를 확인하여 분류기를 구현하였다. 학습에 사용하지 않은 보수 및 진보적 성향의 기사를 사용하여 분류기의 성능을 검증하였다.

• Journal of Information Technology Services
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• v.11 no.1
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• pp.211-221
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• 2012

Many metric-based classification models have been proposed to predict fault-proneness of software module. This paper presents two prediction models using Bayesian classifier which is one of the most popular modern classification algorithms. Bayesian model based on Bayesian probability theory can be a promising technique for software quality prediction. This is due to the ability to represent uncertainty using probabilities and the ability to partly incorporate expert's knowledge into training data. The two models, Na$\ddot{i}$veBayes(NB) and Bayesian Belief Network(BBN), are constructed and dimensionality reduction of training data and test data are performed before model evaluation. Prediction accuracy of the model is evaluated using two prediction error measures, Type I error and Type II error, and compared with well-known prediction models, backpropagation neural network model and support vector machine model. The results show that the prediction performance of BBN model is slightly better than that of NB. For the data set with ambiguity, although the BBN model's prediction accuracy is not as good as the compared models, it achieves better performance than the compared models for the data set without ambiguity.

• Journal of the Korean Society for Nondestructive Testing
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• v.15 no.4
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• pp.531-539
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• 1996

The research for the classification of the artificial defects in welding parts is performed using the pattern recognition technology of ultrasonic signal. The signal pattern recognition package including the user defined function is developed to perform the digital signal processing, feature extraction, feature selection and classifier selection. The neural network classifier and the statistical classifiers such as the linear discriminant function classifier and the empirical Bayesian classifier are compared and discussed. The pattern recognition technique is applied to the classification of artificial defects such as notchs and a hole. If appropriately learned, the neural network classifier is concluded to be better than the statistical classifiers in the classification of the artificial defects.

• Journal of the Korean Society for Nondestructive Testing
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• v.16 no.1
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• pp.34-45
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• 1996

The signal pattern recognition program which can perform signal acquisition and processing, the extraction and selection of features, the classifier design and the evaluation, is developed and applied to the classification of artificial defects in the weld zone of Austenitic STS304. The neural network classifier is compared with the linear discriminant function classifier and the empirical Bayesian classifier. The signal through a broadband sensor is compared with that through a resonance type sensor. In recognition rate, the neural network classifier is best, and the signal through a broadband sensor is better.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.588-591
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• 2003

This paper presents a new short-term load forecasting system using data mining. Since the electric load has very different pattern according to the day, it definitely gives rise to the forecasting error if only one forecasting model is used. Thus, to resolve this problem, the fuzzy model-based classifier and predictor are proposed for the forecasting of the hourly electric load. The proposed classifier is the multi-input and multi-output fuzzy system of which the consequent part is composed of the Bayesian classifier. The proposed classifier attempts to categorize the input electric load into Monday, Tuesday$\sim$Friday, Saturday, and Sunday electric load, Then, we construct the Takagi-Sugeno (T-S) fuzzy model-based predictor for each class. The parameter identification problem is converted into the generalized eigenvalue problem (GEVP) by formulating the linear matrix inequalities (LMIs). Finally, to show the feasibility of the proposed method, this paper provides the short-term load forecasting example.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.04a
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• pp.403-414
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• 2005

Most of the currently used spam-filters are based on a Bayesian classification technique, where some serious problems occur such as a limited precision/recall rate and the false positive error. This paper addresses a solution to the problems using a modified Bayesian classifier based on chi-square statistics. The resulting spam-filter is more accurate and flexible than traditional Bayesian spam-filters and can be a personalized one providing some parameters when the filter is teamed from training data.

• The Korean Journal of Applied Statistics
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• v.31 no.3
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• pp.329-342
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• 2018

Nomogram is a statistical tool that visualizes the risk factors of the disease and then helps to understand the untrained people. This study used risk factors of chronic obstructive pulmonary disease (COPD) and compared with logistic regression model and naïve Bayesian classifier model. Data were analyzed using the Korean National Health and Nutrition Examination Survey 6th (2013-2015). First, we used 6 risk factors about COPD. We constructed nomogram using logistic regression model and naïve Bayesian classifier model. We also compared the nomograms constructed using the two methods to find out which method is more appropriate. The receiver operating characteristic curve and the calibration plot were used to verify each nomograms.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.91-93
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• 2004

B2B Marketplace상에서의 거래에서 나타나는 주요한 특징은 다품종 및 대량의 물품 거래가 n：n거래 관계에 놓여있다는 점과 거래자가 원활한 거래 및 기업 내 관리를 위해 각자의 전자 카탈로그를 이용한 거래를 원한다는 정이다. 하지만 개별적인 전자 카탈로그 사용과 미흡한 표준안은 전자 카탈로그 상호 연계의 걸림돌이 되어 시장 형성의 걸림돌이 되고 있다. B2B Marketplace는 표준 분류체계를 중심으로 거래 대상 상품을 재분류하여 구매 당사자간의 거래 대상 물품에 대한 상호 애핑을 지원하는 방법 등으로 이를 충족시키려 하고 있다. 하지만 요청되는 다량의 물품에 대해 매번 분류를 수행해야 하는 고비용의 작업이라는 문제점이 있다. 본 논문에서는 이를 극복하기 위하여 기계학습 기법을 이용한 전자 카탈로그 상품 자동분류기를 모델링하고 이를 구현하는 것에 초점을 두었다. 상품의 속성별로 분류에 끼치는 영향력이 다론 것이라는데 착안하여 전자 카탈로그를 상품 단위로 재 모델링 하였으며 속성별 정보가 풍부하지 못한 정물 극복하기 위하여 속성값을 어휘 단위로 구분한 데이터를 추가 하는 확장 모델을 정의하였다. 또한 해당 모델을 학습시키기 위한 알고리즘으로는 속성별로 다른 가중치를 부여 할 수 있도록 확장된 Naive Bayesian Classifier를 고안하였다. 그리고 이론 B2B Market Place상의 실 데이터에 적용하여 고안된 모델의 유효성을 검증하였다.