• Journal of Digital Convergence
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• v.11 no.11
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• pp.409-414
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• 2013

Naive Bayesian classifiers are a powerful and well-known type of classifiers that can be easily induced from a dataset of sample cases. However, the strong conditional independence assumptions can sometimes lead to weak classification performance. Normally, naive Bayesian classifiers use Gaussian distributions to handle continuous attributes and to represent the likelihood of the features conditioned on the classes. The probability density of attributes, however, is not always well fitted by a Gaussian distribution. Another eminent type of classifier is the neuro-fuzzy classifier, which can learn fuzzy rules and fuzzy sets using supervised learning. Since there are specific structural similarities between a neuro-fuzzy classifier and a naive Bayesian classifier, the purpose of this study is to apply learning distribution graphs constructed by a neuro-fuzzy network to naive Bayesian classifiers. We compare the Gaussian distribution graphs with the fuzzy distribution graphs for the naive Bayesian classifier. We applied these two types of distribution graphs to classify leukemia and colon DNA microarray data sets. The results demonstrate that a naive Bayesian classifier with fuzzy distribution graphs is more reliable than that with Gaussian distribution graphs.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04b
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• pp.490-492
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• 2002

본 논문에서는 필터링 문제를 이진 문서 분류 문제로 보고 신문기사 필터링에 베이지안 분류자를 사용한다. 신문 기사 필터링 문제에서 베이지안 분류자를 사용할 경우 학습 문서가 고정되어 있지 않기 때문에 여러 가지 파라미터를 사용하여 실험을 하였다. 실험 결과 베이지안 이진 분류기는 제한된 학습 문서에서 더 나은 성능을 보였고 해당 문서 집합에서 10%이상 비율의 문서를 사용자가 선택해야 함을 알 수 있었다.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.212-214
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• 2013

심전도 데이터는 일반적으로 분류기를 사용한 실험이 많으며, QRS-Complex와 R-R interval 간격을 추출하여 실험한다. 본 연구에서는 R-R interval을 추출하였다. 그리고 R-R interval 데이터와 HRV 데이터를 구성하였고, 베이지안 네트워크 분류기를 사용하여 정확도를 도출하였다. 심장관련 데이터는 심전도 뿐 아니라 심장병 데이터도 있는데 심전도 데이터와 같이 분류실험을 시행하여 정확도를 도출하였다. 그리고 베이지안 네트워크분류기의 정확도를 분석하기 위해 타 논문의 실험결과와 비교하였다. 타 논문과 본 연구의 결과를 비교해보니 베이지안 네트워크가 타 결과에 비해서 정확도 도출이 우수하였다.

• The Journal of the Korea Contents Association
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• v.5 no.3
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• pp.227-235
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• 2005

Document filtering is a task of deciding whether a document has relevance to a specified topic. As Internet and Web becomes wide-spread and the number of documents delivered by e-mail explosively grows the importance of text filtering increases as well. In this paper, we treat document filtering problem as binary document classification problem and we proposed the News Filtering system based on the Bayesian Classifier. For we perform filtering, we make an experiment to find out how many training documents, and how accurate relevance checks are needed.

• The KIPS Transactions:PartB
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• v.13B no.7 s.110
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• pp.653-662
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• 2006

In ubiquitous computing that is to build computing environments to provide proper services according to user's context, human being's emotion recognition based on facial expression is used as essential means of HCI in order to make man-machine interaction more efficient and to do user's context-awareness. This paper addresses a problem of rigidly basic emotion recognition in context-sensitive facial expressions through a new Bayesian classifier. The task for emotion recognition of facial expressions consists of two steps, where the extraction step of facial feature is based on a color-histogram method and the classification step employs a new Bayesian teaming algorithm in performing efficient training and test. New context-sensitive Bayesian learning algorithm of EADF(Extended Assumed-Density Filtering) is proposed to recognize more exact emotions as it utilizes different classifier complexities for different contexts. Experimental results show an expression classification accuracy of over 91% on the test database and achieve the error rate of 10.6% by modeling facial expression as hidden context.

• 한국IT서비스학회:학술대회논문집
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• 2002.11a
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• pp.411-416
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• 2002

인터넷의 발전과 더불어 전자메일 사용자가 증가하게 되고, 기업의 고객접촉채널로서 전자메일에 대한 중요성 또한 증가되고 있다. 고객의 요구에 대해 적시에 적절하게 응답하지 못하면 고객의 불만족이 증가하게 되고, 충성도를 감소시켜 결국 장기적 매출 및 수익성 악화를 초래하게 된다. 따라서 고객의 전자메일에 신속, 정확하게 응답할 수 있는 전자 메일 자동관리 시스템의 필요성이 증가되고 있다. 본 연구에서는 나이브 베이지안 학습과 중심점 기반 분류 방법을 이용하여 전자메일 자동관리 시스템에서 전자메일 분류를 수행하는 분류기를 구현한다. 구현된 분류기를 이용하여 실제 기업의 고객 전자메일을 분류하는 실험을 수행하고 두 분류기의 성능을 비교하였다. 실험결과 두 분류기 모두 전자메일 분류에 비교적 우수한 성능을 보였다. 그러나, 클래스 수가 적은 경우 중심점 기반 분류기가 좋은 성능을 보였으나, 학습집합이 작아지면서 두 분류기의 성능 차이는 없었으며, 클래스의 수가 많아지면서 나이브 베이지안 분류기가 더 우수한 성능을 보였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.01a
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• pp.147-148
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• 2018

다중 레이블 분류 문제는 다중 레이블 데이터를 입력받았을 때 연관된 다수의 레이블을 추측하는 문제이다. 본 논문에서는 다중 레이블 분류 문제의 기법 중 하나인 나이브 베이지안 분류기에 레이블 의존성을 계산하여 결과에 반영한 결과 다중 레이블 분류 문제의 성능이 개선됨을 확인하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.4
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• pp.360-365
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• 2014

There are many types of advanced devices for weather prediction process such as weather radar, satellite, radiosonde, and other weather observation devices. Among them, the weather radar is an essential device for weather forecasting because the radar has many advantages like wide observation area, high spatial and time resolution, and so on. In order to analyze the weather radar observation result, we should know the inside structure and data. Some non-precipitation echoes exist inside of the observed radar data. And these echoes affect decreased accuracy of weather forecasting. Therefore, this paper suggests a method that could remove line-shaped non-precipitation echo from raw radar data. The line-shaped echoes are distinguished from the raw radar data and extracted their own features. These extracted data pairs are used as learning data for naive bayesian classifier. After the learning process, the constructed naive bayesian classifier is applied to real case that includes not only line-shaped echo but also other precipitation echoes. From the experiments, we confirm that the conclusion that suggested naive bayesian classifier could distinguish line-shaped echo effectively.

• The KIPS Transactions:PartB
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• v.9B no.5
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• pp.625-634
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• 2002

In this paper, we conducted various experiments with Bayesian networks in order to analyze clinical data of infertility patients. With these experiments, we tried to find out inter-dependencies among important factors playing the key role in clinical pregnancy, and to compare 3 different kinds of Bayesian network classifiers (including NBN, BAN, GBN) in terms of classification performance. As a result of experiments, we found the fact that the most important features playing the key role in clinical pregnancy (Clin) are indication (IND), stimulation, age of female partner (FA), number of ova (ICT), and use of Wallace (ETM), and then discovered inter-dependencies among these features. And we made sure that BAN and GBN, which are more general Bayesian network classifiers permitting inter-dependencies among features, show higher performance than NBN. By comparing Bayesian classifiers based on probabilistic representation and reasoning with other classifiers such as decision trees and k-nearest neighbor methods, we found that the former show higher performance than the latter due to inherent characteristics of clinical domain. finally, we suggested a feature reduction method in which all features except only some ones within Markov blanket of the class node are removed, and investigated by experiments whether such feature reduction can increase the performance of Bayesian classifiers.

• Journal of IKEEE
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• v.8 no.2 s.15
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• pp.216-223
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• 2004

The customer's bulletin board is the important channel to get opinions from customers directly. The effective management of the bulletin board for the customer improves the reliance by providing the best replies and by accepting opinions of the customer and furthermore, that can raise the customer's reliance of the whole shopping mall is the important eCRM method. But, the present mostly customer's bulletin board is been replied without any classifying about many kinds of question. Consequently, The shopping mall should do systematic management of the best professional reply about many kinds of question. In order to resolve this problem, we implement a classifier called Naive Bayesian classifier is classified automatically bulletin board for eCRM of shopping mall.