| dc.contributor | 廣播電視學系 | - |
| dc.creator (作者) | Sheu, Jyh-Jian | en-US |
| dc.creator (作者) | 許志堅 | zh-tw |
| dc.creator (作者) | Chu, Ko-Tsung | en-US |
| dc.creator (作者) | Lee, Cheng-Chi | en-US |
| dc.creator (作者) | Li, Nien-Feng | en-US |
| dc.date (日期) | 2017 | - |
| dc.date.accessioned | 27-Jul-2017 12:54:39 (UTC+8) | - |
| dc.date.available | 27-Jul-2017 12:54:39 (UTC+8) | - |
| dc.date.issued (上傳時間) | 27-Jul-2017 12:54:39 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/111428 | - |
| dc.description.abstract (摘要) | This research manages in-depth analysis on the knowledge about spams and expects to propose an efficient spam filtering method with the ability of adapting to the dynamic environment. We focus on the analysis of email`s header and apply decision tree data mining technique to look for the association rules about spams. Then, we propose an efficient systematic filtering method based on these association rules. Our systematic method has the following major advantages: (1) Checking only the header sections of emails, which is different from those spam filtering methods at present that have to analyze fully the email`s content. Meanwhile, the email filtering accuracy is expected to be enhanced. (2) Regarding the solution to the problem of concept drift, we propose a window-based technique to estimate for the condition of concept drift for each unknown email, which will help our filtering method in recognizing the occurrence of spam. (3) We propose an incremental learning mechanism for our filtering method to strengthen the ability of adapting to the dynamic environment. © 2017 Sheu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | - |
| dc.format.extent | 1125791 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.relation (關聯) | PLoS ONE, 12(2), 論文編號 e0171518 | - |
| dc.subject (關鍵詞) | data mining; decision tree; e-mail; filtration; learning | - |
| dc.title (題名) | An efficient incremental learning mechanism for tracking concept drift in spam filtering | en-US |
| dc.type (資料類型) | article | - |
| dc.identifier.doi (DOI) | 10.1371/journal.pone.0171518 | - |
| dc.doi.uri (DOI) | http://dx.doi.org/10.1371/journal.pone.0171518 | - |
