An incremental cluster-based approach to spam filtering

Hsiao, Wen-Feng; Chang, Te-Min

doi:10.1016/j.eswa.2007.01.018

Cited by 43 publications

(26 citation statements)

References 12 publications

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“…The larger the value of z, the more significant is the difference between the two means and therefore the larger the value discriminatory power of the attribute in classifying the dataset as spam/legitimate. Sorting the list of input attributes in a descending order based on the values of their Z statistic gives the following ranking: {21, 25 Attributes toward the beginning of the list should prove more effective than attributes toward the end. However, this simple ranking approach does not take into account possible mutual correlation between various attributes when grouped together to classify the dataset.…”

Section: Description Of the Datasetmentioning

confidence: 99%

“…Among these methods, Bayesian classifiers have been widely applied as one of the most effective spam filters [19,23]. Recent reviews and taxonomy of current and potential solutions for the spam problem are presented in [9,18,21,25,28,44]. Although empirical studies on existing spam filters have reported promising results in terms of detection accuracy, the number of false positives (legitimate messages classified as spam) is still unacceptably high.…”

Section: Introductionmentioning

confidence: 99%

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Using GMDH-based networks for improved spam detection and email feature analysis

El-Alfy

Abdel-Aal

2011

Applied Soft Computing

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Section: Description Of the Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using GMDH-based networks for improved spam detection and email feature analysis

El-Alfy

Abdel-Aal

2011

Applied Soft Computing

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“…Application of methods of clustering analyses to the problem of filtering e-mails to legitimate and spam is considered in papers [15][16][17][18]. From 2009 year, beginning from Paulo Cortez's, et al article [19] one can meet the statement as a Symbiotic Data Mining which is a hybrid of Collaborative Filtering (CF) and ContentBased Filtering (CBF).…”

Section: Historical Review Of Spam Filtering Methodsmentioning

confidence: 99%

Survey on Spam Filtering Techniques

Nazirova¹

2011

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In the recent years spam became as a big problem of Internet and electronic communication. There developed a lot of techniques to fight them. In this paper the overview of existing e-mail spam filtering methods is given. The classification, evaluation, and comparison of traditional and learning-based methods are provided. Some personal anti-spam products are tested and compared. The statement for new approach in spam filtering technique is considered.

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“…For example, concept drift in textual data streams can be identified by monitoring word frequencies (Swan and Allan 1999) and the formation of new word clusters (Hsiao and Chang 2008;Spinosa et al 2007). Kifer et al (2004) introduce a more generic approach that uses a two window paradigm to detect changes in feature distribution.…”

Section: Triggered Rebuildmentioning

confidence: 99%

Drift detection using uncertainty distribution divergence

Lindstrom¹,

Namee²,

Delany³

2012

Evolving Systems

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Data generated from naturally occurring processes tends to be nonstationary. For example, seasonal and gradual changes in climate data and sudden changes in financial data. In machine learning the degradation in classifier performance due to such changes in the data is known as concept drift and there are many approaches to detecting and handling it. Most approaches to detecting concept drift, however, make the assumption that true classes for test examples will be available at no cost shortly after classification and base the detection of concept drift on measures relying on these labels. The high labelling cost in many domains provides a strong motivation to reduce the number of labelled instances required to detect and handle concept drift. Triggered detection approaches that do not require labelled instances to detect concept drift show great promise for achieving this. In this paper we present Confidence Distribution Batch Detection (CDBD), an approach that provides a signal correlated to changes in concept without using labelled data. This signal combined with a trigger and a rebuild policy can maintain classifier accuracy which, in most cases, matches the accuracy achieved using classification error based detection techniques but using only a limited amount of labelled data.

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An incremental cluster-based approach to spam filtering

Cited by 43 publications

References 12 publications

Using GMDH-based networks for improved spam detection and email feature analysis

Using GMDH-based networks for improved spam detection and email feature analysis

Survey on Spam Filtering Techniques

Drift detection using uncertainty distribution divergence

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