Feature Selection by Fuzzy Inference and Its Application to Spam-Mail Filtering

Kim, Jongwan; Kang, Sin-Jae

doi:10.1007/11596448_52

Cited by 4 publications

(4 citation statements)

References 4 publications

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“…The greater the average value of all matched term ratio in a rule set is, the simpler and more easily interpretable the rule set is to humans. For example, a test instance, (Age = JS and ECat = Adults and RHit = S and Adults = F and Games = T and Jobs = T) and [10] = 6/4 = 1.5. Therefore, the R10 rule has a greater matched term ratio than R9 in terms of quantified comprehensibility.…”

Section: Methodsmentioning

confidence: 99%

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Constructing a User Preference Ontology for Anti-spam Mail Systems

Kim

Dou

Liu

et al. 2007

Advances in Artificial Intelligence

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Abstract. The judgment that whether an email is spam or non-spam may vary from person to person. Different individuals can have totally different responses to the same email based on their preferences. This paper presents an innovative approach that incorporates user preferences to construct an anti-spam mail system, which is different from the conventional content-based approaches. We build a user preference ontology to formally represent the important concepts and rules derived from a data mining process. Then we use an inference engine that utilizes the knowledge to predict the user's action on new incoming emails. We also suggest a new rule optimization procedure inspired from logic synthesis to improve comprehensibility and exclude redundant rules. Experimental results showed that our user preference based architecture achieved good performance and the rules derived from the architecture and the optimization method have better quality in terms of comprehensibility.

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Section: Methodsmentioning

confidence: 99%

“…The first author has conducted several experiments on contentbased email filtering [10]. From his previous experience, we aimed at developing an anti-spam mail system based on personal interests and behaviors from a specific user or user group instead of mere analysis of the contents of the email header and body.…”

Section: Data Preparationmentioning

confidence: 99%

Constructing a User Preference Ontology for Anti-spam Mail Systems

Kim

Dou

Liu

et al. 2007

Advances in Artificial Intelligence

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“…The problem of email classification has been addressed by many researchers with different perspectives (Balakumar & Vaidehi, ; El‐alfy & Al‐Qunaieer, ; Fuad et al ., ; Clark et al ., ; Clark et al ., ; Kim & Kang, ; Koprinska et al ., ; Seongwook Youn and McLeod, 2007; Seongwook. Youn and McLeod, 2007; Upasana & Chakravarhty, ; Upasana & Chakravarty, ; Wang & Cloete, ).…”

Section: Introductionmentioning

confidence: 99%

“…Youn and McLeod, 2007; Upasana & Chakravarhty, ; Upasana & Chakravarty, ; Wang & Cloete, ). Only a few works (Fuad et al ., ; Kim & Kang, ; El‐alfy & Al‐Qunaieer, ) have actually exploited the content of the email, and all of them have addressed spam email filtering. The rest of the previously cited work has utilized the words or keywords from the email text and have gone for bag of word‐based approaches using simple structural attributes for classification.…”

Section: Introductionmentioning

confidence: 99%

Context‐based email classification model

2015

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Context‐based email classification requires understanding of semantic and structural attributes of email. Most of the research has focused on generating semantic properties through structural components of email. By viewing emails as events (as a major subset of class of email), a rich contextual test‐bed representation for understanding of the semantic attributes of emails has been devised. The event‐ based emails have traditionally been studied based on simple structural properties. In this paper, we present a novel approach by first representing such class of emails as graphs, followed by heuristically applying graph mining and matching algorithm to pick templates representing contextual and semantic attributes that help classify emails. The classification templates used three key event classes: social, personal and professional. Results show that our graph mining and matching supported template‐based approach performs consistently well over event email data set with high accuracy.

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A Survey on Text Classification Techniques for E-mail Filtering

Pandey¹,

Chakravarty

2010

2010 Second International Conference on Machine Learning and Computing

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Feature Selection by Fuzzy Inference and Its Application to Spam-Mail Filtering

Cited by 4 publications

References 4 publications

Constructing a User Preference Ontology for Anti-spam Mail Systems

Constructing a User Preference Ontology for Anti-spam Mail Systems

Context‐based email classification model

A Survey on Text Classification Techniques for E-mail Filtering

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