Training Multiple Support Vector Machines for Personalized Web Content Filters

Dung Duc NGUYEN  Maike ERDMANN  Tomoya TAKEYOSHI  Gen HATTORI  Kazunori MATSUMOTO  Chihiro ONO  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E96-D   No.11   pp.2376-2384
Publication Date: 2013/11/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E96.D.2376
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Artificial Intelligence, Data Mining
Keyword: 
support vector machines,  sequential minimal optimization,  text categorization,  Web content filtering,  

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Summary: 
The abundance of information published on the Internet makes filtering of hazardous Web pages a difficult yet important task. Supervised learning methods such as Support Vector Machines (SVMs) can be used to identify hazardous Web content. However, scalability is a big challenge, especially if we have to train multiple classifiers, since different policies exist on what kind of information is hazardous. We therefore propose two different strategies to train multiple SVMs for personalized Web content filters. The first strategy identifies common data clusters and then performs optimization on these clusters in order to obtain good initial solutions for individual problems. This initialization shortens the path to the optimal solutions and reduces the training time on individual training sets. The second approach is to train all SVMs simultaneously. We introduce an SMO-based kernel-biased heuristic that balances the reduction rate of individual objective functions and the computational cost of kernel matrix. The heuristic primarily relies on the optimality conditions of all optimization problems and secondly on the pre-calculated part of the whole kernel matrix. This strategy increases the amount of information sharing among learning tasks, thus reduces the number of kernel calculation and training time. In our experiments on inconsistently labeled training examples, both strategies were able to predict hazardous Web pages accurately (> 91%) with a training time of only 26% and 18% compared to that of the normal sequential training.