A Time-Sensitive Spam Filter Algorithm Dealing with Concept-drift

DOI

10.2991/icmmct-16.2016.248How to use a DOI?

Keywords

Data Stream; Classification; Concept-drift

Abstract

Spam, under a variety of shapes and forms, continues to inflict increased damage. Varying machine learning techniques have played an important role in spam filtering field in condition that ample training data is available to build a robust classifier. These methods include Decision Tree, Support Vector Machine (SVM), etc. However, spam filtering is a particularly challenging task as the data distribution and concept being learned changes over time. More seriously, data stream classification poses many challenges to the data mining community. In this paper, we proposed a time-sensitive spam filter algorithm dealing with concept-drift (TSSFA), which is appropriate for such dynamically changing contexts. We evaluate its performance on the TREC public corpus, and showed satisfactory result.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jiaolong Liu
PY  - 2016/03
DA  - 2016/03
TI  - A Time-Sensitive Spam Filter Algorithm Dealing with Concept-drift
BT  - Proceedings of the 2016 4th International Conference on Machinery, Materials and Computing Technology
PB  - Atlantis Press
SP  - 1263
EP  - 1268
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmmct-16.2016.248
DO  - 10.2991/icmmct-16.2016.248
ID  - Liu2016/03
ER  -