Abstract
Now-a-days, email is often one of the most widely used means of communication despite the rise of other communication methods such as instant messaging or communication via social networks. The need to automate the email stream management increases for reasons such as multi-folder categorization, and spam email classification. There are solutions based on email content, capable of contemplating elements such as the text subjective nature, adverse effects of concept drift, among others. This paper presents an email stream classifier with a goal-oriented approach to client and server environment. The i* language was the basis for designing the proposed email stream classifier. The email environment was represented with the early requirements model and the proposed classifier with the late requirements model. The classifier was implemented following a multi-agent system approach supported by JADE agent platform and Implementation_JADE pattern. The behavior of agents was taking from an existing classifier. The multi-agent classifier was evaluated using functional, efficacy and performance tests, which compared the existing classifier with the multi-agent approach. The results obtained were satisfactory in all the tests. The performance of multi-agent approach was better than the existing classifier due to the use of multi-threads.
Highlights
Email is one of the most widely used services by Internet users
This decrease in time is due to the use of multi-threads incorporated by JADE
With aspects of social modeling and the language i* was representing actors, goals, tasks, resources and dependencyrelations existing among actors of the email environment and designing an email stream classifier by following a goaloriented approach
Summary
Email is one of the most widely used services by Internet users. the growth in the number of users makes this service grow as well. [1]. Email classification can be considered a goal that serves as a means to satisfy other goals An example of this is the email filtering that the mail user agent performs in the client application or the spam email detector on the server. This includes other actors who relate to each other to achieve proper functioning, an aspect that could be represented through social modelling [5]. Proposals facing the challenge of increasing the adaptive capacity of email classification solutions tend to focus on specific modules [12, 13, 14, 15, 6].
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