A comparative study on feature selection and adaptive strategies for email foldering

Abstract

Email foldering is a challenging problem mainly due to its high dimensionality and dynamic nature. This work presents a comparison of several feature extraction/ selection and adaptive strategies aimed at coping with these two main difficulties. To this end, several studies have been carried out using the ENRON email dataset in order to test how different configuration settings for feature extraction/selection and adapting processes can affect the classification performance.

Improving Email Response in an Email Management System Using Natural Language Processing Based Probabilistic Methods

“…Users can communicate with each other following different formal and informal standards and an algorithm for accurate classification in such circumstances must adhere to adaptive classification to work in such a complex environment (Carmona-Cejudo et al, 2011). Chan et al (22004) proposed that there may be an attribute that could be extracted to relate text sections of an email together, but this is yet to be completely optimized due to limitations of Natural Language Processing.…”