A Time-Sensitive Spam Filter Algorithm Dealing with Concept-drift
- 10.2991/icmmct-16.2016.248How to use a DOI?
- Data Stream; Classification; Concept-drift
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.
- © 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/).
Cite this article
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 -