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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Hailong Li |
| Copyright Year | 2014 |
| Description | Author affiliation: Sch. of Comput. Sci. & Eng., Beihang Univ., Beijing, China (Hailong Li) |
| Abstract | Web spamming is the deliberate manipulation of search engine indexes to make a page get high ranking than which it deserved considering its true value. Since the evolution of web spam, a new based on machine learning algorithm web spam detection method which has self-learning ability has emerged. Web spam detection is viewed as a binary classification learning problem. Because labeled training examples are fairly expensive to obtain which need the participation of experts in this field and labor costs, how to fully utilize a large number of unlabeled web page examples on the web is a challenge faced by web spam detection. In this paper, we present a web spam detection algorithm according to improve tri-training. It uses a small amount of labeled examples and a large number of unlabeled examples to train classifiers, which can reduce the cost of labeled examples and improve the learning performance. Both web page content features and link features are used in this paper. |
| Starting Page | 61 |
| Ending Page | 65 |
| File Size | 14735015 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781479920334 |
| e-ISBN | 9781479920303 |
| DOI | 10.1109/PIC.2014.6972296 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-05-16 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Algorithm design and analysis Tri-training Unsolicited electronic mail Search engine Feature view Classification algorithms Web spam detection Training Co-training Web pages Search engines Prediction algorithms Web spam |
| Content Type | Text |
| Resource Type | Article |
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