A Large-Scale Evaluation of an E-mail Management Assistant

Author

Wobcke, Wayne ; Krzywicki, Alfred ; Chan, Yiu-Wa Rita

Author_Institution

Sch. of Comput. Sci. & Eng., Univ. of New South Wales, Sydney, NSW

Volume

2

fYear

2008

fDate

9-12 Dec. 2008

Firstpage

438

Lastpage

442

Abstract

EMMA is an e-mail management assistant based on ripple down rules, providing a high degree of classification accuracy while simplifying the task of maintaining the consistency of the rule base. A naive Bayes algorithm is used to improve the usability of EMMA by suggesting keywords to help the user define rules. In this paper, we report on an experimental evaluation of EMMA on 16 998 pre-classified messages. The aim of the evaluation was to show that the ripple down rule technique used in EMMA applies to large-scale data sets in realistic organizational contexts. The results showed conclusively that EMMA attained the agreed success criteria for the evaluation and that the knowledge acquisition method used in EMMA outperforms standard machine learning methods.

Keywords

Bayes methods; electronic mail; knowledge acquisition; e-mail management assistant; knowledge acquisition method; naive Bayes algorithm; pre-classified messages; ripple down rules; standard machine learning methods; user define rules; Conference management; Electronic mail; Engineering management; Intelligent agent; Knowledge acquisition; Large-scale systems; Learning systems; Sorting; Technology management; Usability;

fLanguage

English

Publisher

ieee

Conference_Titel

Web Intelligence and Intelligent Agent Technology, 2008. WI-IAT '08. IEEE/WIC/ACM International Conference on

Conference_Location

Sydney, NSW

Print_ISBN

978-0-7695-3496-1

Type

conf

DOI

10.1109/WIIAT.2008.75

Filename

4740662