Learning to classify e-mail

Koprinska, Irena; Poon, Josiah; Clark, James J.; Chan, Jason W.

doi:10.1016/j.ins.2006.12.005

Cited by 143 publications

(72 citation statements)

References 20 publications

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“…The random forests categorizers created in our study of the WEKA categorizers performed well in labeling a high percentage of true positives and relatively few false negatives. Other research groups have also found that random forest categorizers perform well for the related problem of categorization of spam in e-mail messages (Koprinska, 2007).…”

Section: Random Forestsmentioning

confidence: 96%

Yucca Mountain licensing support network archive assistant.

Dunlavy¹,

Bauer²,

Verzi³

et al. 2008

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This report describes the Licensing Support Network (LSN) Assistant-a set of tools for categorizing e-mail messages and documents, and investigating and correcting existing archives of categorized e-mail messages and documents. The two main tools in the LSN Assistant are the LSN Archive Assistant (LSNAA) tool for recategorizing manually labeled e-mail messages and documents and the LSN Realtime Assistant (LSNRA) tool for categorizing new e-mail messages and documents. This report focuses on the LSNAA tool.There are two main components of the LSNAA tool. The first is the Sandia Categorization Framework, which is responsible for providing categorizations for documents in an archive and storing them in an appropriate Categorization Database. The second is the actual user interface, which primarily interacts with the 4 Categorization Database, providing a way for finding and correcting categorizations errors in the database.A procedure for applying the LSNAA tool and an example use case of the LSNAA tool applied to a set of e-mail messages are provided. Performance results of the categorization model designed for this example use case are presented.5

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Section: Random Forestsmentioning

confidence: 96%

Yucca Mountain licensing support network archive assistant.

Dunlavy¹,

Bauer²,

Verzi³

et al. 2008

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“…Much work has been done in the area of spam classification, e.g., using different learning algorithms, such as: rule learners, support vector machines, instance-based learners, decision trees, and stacking [1,7,9,13,29]. More recently, Koprinska et al [22] investigate the performance of the random forest algorithm for the same type of problem, claiming that it outperforms some of the earlier mentioned algorithms on several problems.…”

Section: Related Workmentioning

confidence: 99%

Learning to detect spyware using end user license agreements

et al. 2010

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The amount of software that hosts spyware has increased dramatically. To avoid legal repercussions, the vendors need to inform users about inclusion of spyware via end user license agreements (EULAs) during the installation of an application. However, this information is intentionally written in a way that is hard for users to comprehend. We investigate how to automatically discriminate between legitimate software and spyware associated software by mining EULAs. For this purpose, we compile a data set consisting of 996 EULAs out of which 9.6% are associated to spyware. We compare the performance of 17 learning algorithms with that of a baseline algorithm on two data sets based on a bag-of-words and a meta data model. The majority of learning algorithms significantly outperform the baseline regardless of which data representation is used. However, a non-parametric test indicates that bag-of-words is more suitable than the meta model. Our conclusion is that automatic EULA classification can be applied to assist users in making informed decisions about whether to install an application without having read the EULA. We therefore outline the design of a spyware prevention tool and suggest how to select suitable learning algorithms for the tool by using a multi-criteria evaluation approach.

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“…; rule learners, support vector machines, instance-based learners, decision trees, and stacking [5][6] [7][8] [9]. A more recent study investigates the performance of random forests for the same type of problem claiming that this algorithm outperforms some of the earlier mentioned algorithms on several problems [10].Yet another study applies an unsupervised feature selection algorithm and clustering to classify unlabeled documents [11].…”

Section: Related Workmentioning

confidence: 99%