• Title/Summary/Keyword: 나이브 베이지안 필터링

Search Result 9, Processing Time 0.029 seconds

Spam-Mail Filtering System by Using Naive Bayesian Classifier and Mail Address Validation Check (나이브 베이지안 분류자와 메일 주소 유효성 검사를 이용한 스팸 메일 필터링 시스템)

  • Lim Jung-Taek;Kim Hyung-Joon;Kang Seung-Shik
    • Proceedings of the Korean Information Science Society Conference
    • /
    • 2005.11b
    • /
    • pp.523-525
    • /
    • 2005
  • 본 논문에서는 가중치가 부여된 나이브 베이지안 분류자와 스팸 메일의 특성을 이용한 주소 유효성 검사를 결합하여 필터링하는 방식의 스팸 메일 필터링 시스템을 제안하였다. 주소 유효성 검사를 통해 스팸 메일을 효율적으로 필터링 할 수 있으며, 나이브 베이지안 분류자에 가중치를 부여함으로써 더욱 효과적인 분류를 할 수 있다. 또한, 각 요인의 중요도에 따라 다른 비중을 부여함으로써 메일의 특성을 고려한 필터링 환경을 구현하였다. 실험에서는 제안하는 요인들이 실제로 필터링 성능 향상에 어떤 영향을 미치는지 살펴보고 최적의 시스템 성능을 측정하였다.

  • PDF

Semantics Environment for U-health Service driven Naive Bayesian Filtering for Personalized Service Recommendation Method in Digital TV (디지털 TV에서 시멘틱 환경의 유헬스 서비스를 위한 나이브 베이지안 필터링 기반 개인화 서비스 추천 방법)

  • Kim, Jae-Kwon;Lee, Young-Ho;Kim, Jong-Hun;Park, Dong-Kyun;Kang, Un-Gu
    • Journal of the Korea Society of Computer and Information
    • /
    • v.17 no.8
    • /
    • pp.81-90
    • /
    • 2012
  • For digital TV, the recommendation of u-health personalized service of semantic environment should be done after evaluating individual physical condition, illness and health condition. The existing recommendation method of u-health personalized service of semantic environment had low user satisfaction because its recommendation was dependent on ontology for analyzing significance. We propose the personalized service recommendation method based on Naive Bayesian Classifier for u-health service of semantic environment in digital TV. In accordance with the proposed method, the condition data is inferred by using ontology, and the transaction is saved. By applying naive bayesian classifier that uses preference information, the service is provided after inferring based on user preference information and transaction formed from ontology. The service inferred based on naive bayesian classifier shows higher precision and recall ratio of the contents recommendation rather than the existing method.

Spam-mail Filtering System Using Naive Bayesian Classifier and Message Rule (나이브 베이지안 분류자와 메세지 규칙을 이용한 스팸메일 필터링 시스템)

  • 조한철;조근식
    • Proceedings of the Korean Information Science Society Conference
    • /
    • 2002.04b
    • /
    • pp.223-225
    • /
    • 2002
  • 인터넷의 급속한 성장과 함께 E-Mail은 대표적인 통신수단의 하나가 되어버렸다. 편리하다는 점을 이용해서 엄청난 양의 스팸메일이 매일같이 쏟아져 오고 , 그 문제점의 심각성에 정보통신부에서 정보통신망 이용촉진 및 정보보호 등에 관한 법률이라는 새로운 법률까지 생겨났다. 본 논문에서는 이 법률에서 요구하는 '광고'라는 문구를 걸러내는 등의 메시지 규칙을 갖는 시스템과 기존의 문서 분류에 널리 쓰이던 나이브 베이지안 분류자(Naive Baesian Classifier)를 결합한 스팸 메일 필터링 시스템(Spam-mail Fitering System)을 제안한다. 제안된 시스템에서는 사용자가 직접 규칙을 작성할 필요없이 학습한 데이터를 갖고 자동으로 스팸메일을 분류할 수가 있다. 들어온 메일은 메시지 규칙 기반 필터가 먼저 적용되고, 메세지 규칙 기반 필터에서 분류되지 않으면 나이브 베이지안 필터에서 분류된다. 실험에서는 제안된 시스템의 성능을 평가하기 위해서 메시지 규칙을 사용한 시스템 및 나이브 베이지만 분류자 시스템과 비교 평가하였다. 또한 임계치를 변경함으로써 제안된 시스템의 성능을 높일 수있도록 하였다.

  • PDF

Spam-Mail Filtering System Using Weighted Bayesian Classifier (가중치가 부여된 베이지안 분류자를 이용한 스팸 메일 필터링 시스템)

  • 김현준;정재은;조근식
    • Journal of KIISE:Software and Applications
    • /
    • v.31 no.8
    • /
    • pp.1092-1100
    • /
    • 2004
  • An E-mails have regarded as one of the most popular methods for exchanging information because of easy usage and low cost. Meanwhile, exponentially growing unwanted mails in user's mailbox have been raised as main problem. Recognizing this issue, Korean government established a law in order to prevent e-mail abuse. In this paper we suggest hybrid spam mail filtering system using weighted Bayesian classifier which is extended from naive Bayesian classifier by adding the concept of preprocessing and intelligent agents. This system can classify spam mails automatically by using training data without manual definition of message rules. Particularly, we improved filtering efficiency by imposing weight on some character by feature extraction from spam mails. Finally, we show efficiency comparison among four cases - naive Bayesian, weighting on e-mail header, weighting on HTML tags, weighting on hyperlinks and combining all of four cases. As compared with naive Bayesian classifier, the proposed system obtained 5.7% decreased precision, while the recall and F-measure of this system increased by 33.3% and 31.2%, respectively.

Documents Filtering and Topic Prediction for SNS using Naïve Bayesian Classifier and MapReduce (나이브 베이지안 분류기와 MapReduce 를 이용한 SNS 문서 필터링 및 토픽 예측)

  • Park, Hosik;Kang, Namyong;Park, Seulgi;Moon, Jungmin;Oh, Sangyoon
    • Proceedings of the Korea Information Processing Society Conference
    • /
    • 2014.04a
    • /
    • pp.109-111
    • /
    • 2014
  • SNS(Social Network Service)는 새로운 소통수단으로 인적 네트워크뿐만 아니라 사회, 문화 등에 많은 영향을 미치고 있다. 특히, 무선인터넷과 스마트폰의 보급으로 정보유통량이 기하급수적으로 증가하면서, 데이터를 처리 및 분석하는 것이 화두가 되고 있다. 본 논문에서는 급증하는 SNS 데이터를 처리 및 분석하여 의미 있는 데이터를 키워드 중심으로 추출하고자 하였다. 이를 위해 기존 데이터 처리방식이 아닌 빅데이터 처리에 적합한 MapReduce 환경에서 SNS 데이터를 필터링하고, 토픽을 예측하기 처리방법을 제시하였다. 또한, 웹 서비스를 기반으로 구현하여 분석된 데이터를 시각적으로 표현하고, 재생산하였으며, 실험을 통해 제안하는 처리방법의 성능을 검증하였다.

Features Reduction using Logistic Regression for Spam Filtering (로지스틱 회귀 분석을 이용한 스펨 필터링의 특징 축소)

  • Jung, Yong-Gyu;Lee, Bum-Joon
    • The Journal of the Institute of Internet, Broadcasting and Communication
    • /
    • v.10 no.2
    • /
    • pp.13-18
    • /
    • 2010
  • Today, The much amount of spam that occupies the mail server and network storage occurs the lack of negative issues, such as overload, and for users to delete the spam should spend time, resources have a problem. Automatic spam filtering on the incidence to solve the problem is essential. A lot of Spam filters have tried to solve the problem emerged as an essential element automatically. Unlike traditional method such as Naive Bayesian, PCA through the many-dimensional data set of spam with a few spindle-dimensional process that narrowed the operation to reduce the burden on certain groups for classification Logistic regression analysis method was used to filter the spam. Through the speed and performance, it was able to get the positive results.

Game Recommendation System Based on User Ratings (사용자 평점 기반 게임 추천 시스템)

  • Kim, JongHyen;Jo, HyeonJeong;Kim, Byeong Man
    • Journal of Korea Society of Industrial Information Systems
    • /
    • v.23 no.6
    • /
    • pp.9-19
    • /
    • 2018
  • As the recent developments in the game industry and people's interest in game streaming become more popular, non-professional gamers are also interested in games and buying them. However, it is difficult to judge which game is the most enjoyable among the games released in dozens every day. Although the game sales platform is equipped with the game recommendation function, it is not accurate because it is used as a means of increasing their sales and recommending users with a focus on their discount products or new products. For this reason, in this paper, we propose a game recommendation system based on the users ratings, which raises the recommendation satisfaction level of users and appropriately reflect their experience. In the system, we implement the rate prediction function using collaborative filtering and the game recommendation function using Naive Bayesian classifier to provide users with quick and accurate recommendations. As the result, the rate prediction algorithm achieved a throughput of 2.4 seconds and an average of 72.1 percent accuracy. For the game recommendation algorithm, we obtained 75.187 percent accuracy and were able to provide users with fast and accurate recommendations.

A proper folder recommendation technique using frequent itemsets for efficient e-mail classification (효과적인 이메일 분류를 위한 빈발 항목집합 기반 최적 이메일 폴더 추천 기법)

  • Moon, Jong-Pil;Lee, Won-Suk;Chang, Joong-Hyuk
    • Journal of the Korea Society of Computer and Information
    • /
    • v.16 no.2
    • /
    • pp.33-46
    • /
    • 2011
  • Since an e-mail has been an important mean of communication and information sharing, there have been much effort to classify e-mails efficiently by their contents. An e-mail has various forms in length and style, and words used in an e-mail are usually irregular. In addition, the criteria of an e-mail classification are subjective. As a result, it is quite difficult for the conventional text classification technique to be adapted to an e-mail classification efficiently. An e-mail classification technique in a commercial e-mail program uses a simple text filtering technique in an e-mail client. In the previous studies on automatic classification of an e-mail, the Naive Bayesian technique based on the probability has been used to improve the classification accuracy, and most of them are on an e-mail in English. This paper proposes the personalized recommendation technique of an email in Korean using a data mining technique of frequent patterns. The proposed technique consists of two phases such as the pre-processing of e-mails in an e-mail folder and the generating a profile for the e-mail folder. The generated profile is used for an e-mail to be classified into the most appropriate e-mail folder by the subjective criteria. The e-mail classification system is also implemented, which adapts the proposed technique.

Geographical Name Denoising by Machine Learning of Event Detection Based on Twitter (트위터 기반 이벤트 탐지에서의 기계학습을 통한 지명 노이즈제거)

  • Woo, Seungmin;Hwang, Byung-Yeon
    • KIPS Transactions on Software and Data Engineering
    • /
    • v.4 no.10
    • /
    • pp.447-454
    • /
    • 2015
  • This paper proposes geographical name denoising by machine learning of event detection based on twitter. Recently, the increasing number of smart phone users are leading the growing user of SNS. Especially, the functions of short message (less than 140 words) and follow service make twitter has the power of conveying and diffusing the information more quickly. These characteristics and mobile optimised feature make twitter has fast information conveying speed, which can play a role of conveying disasters or events. Related research used the individuals of twitter user as the sensor of event detection to detect events that occur in reality. This research employed geographical name as the keyword by using the characteristic that an event occurs in a specific place. However, it ignored the denoising of relationship between geographical name and homograph, it became an important factor to lower the accuracy of event detection. In this paper, we used removing and forecasting, these two method to applied denoising technique. First after processing the filtering step by using noise related database building, we have determined the existence of geographical name by using the Naive Bayesian classification. Finally by using the experimental data, we earned the probability value of machine learning. On the basis of forecast technique which is proposed in this paper, the reliability of the need for denoising technique has turned out to be 89.6%.