Web spam detection based on improved tri-training

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.

Improvement of Roller Bearing Diagnosis with Unlabeled Data Using Cut Edge Weight Confidence Based Tritraining

“…Through EEMD process, the original vibration signals collected from the two cases are transformed into two feature sets. According to [18], two parameters of EEMD, that is, the ratio of the standard deviation of the added noise and that of input, are set to be 0.15 and ensemble number is set to be 100. Information of the feature sets is tabulated in 340 examples are then put into and other 292 examples are put into without their labels.…”

“…In addition, it possesses the merits of good efficiency and generalization ability. Tritraining has been successfully applied in Chinese chunking [16], biomedical named entity recognition [17], and web spam detection [18]. With all these advantages and successful application in other areas, tritraining is supposed to be a promising method in bearing fault diagnosis too.…”