Modeling Personalized Email Prioritization: Classification-based and Regression-based Approaches
Email overload, even after spam filtering, presents a serious productivity challenge for busy professionals and executives. One solution is automated prioritization of incoming emails to ensure the most important are read and processed quickly, while others are processed later as/if time permits in declining priority levels. This paper presents a study of machine learning approaches to email prioritization into discrete levels, comparing ordinal regression versus classier cascades. Given the ordinal nature of discrete email priority levels, SVM ordinal regression would be expected to perform well, but surprisingly a cascade of SVM classifiers significantly outperforms ordinal regression for email prioritization. In contrast, SVM regression performs well -- better than classifiers -- on selected UCI data sets. This unexpected performance inversion is analyzed and results are presented, providing core functionality for email prioritization systems.
- Research Organization:
- Brookhaven National Laboratory (BNL), Upton, NY (United States)
- Sponsoring Organization:
- USDOE SC OFFICE OF SCIENCE (SC)
- DOE Contract Number:
- DE-AC02-98CH10886
- OSTI ID:
- 1049211
- Report Number(s):
- BNL-96305-2011-CP; R&D Project: LDRD # 10-001; TRN: US201217%%548
- Resource Relation:
- Conference: CIKM"ll; Glasgow, Scotland; 20111024 through 20111028
- Country of Publication:
- United States
- Language:
- English
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