Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

5-2010

Abstract

Obtaining high-quality and up-to-date labeled data can be difficult in many real-world machine learning applications, especially for Internet classification tasks like review spam detection, which changes at a very brisk pace. For some problems, there may exist multiple perspectives, so called views, of each data sample. For example, in text classification, the typical view contains a large number of raw content features such as term frequency, while a second view may contain a small but highly-informative number of domain specific features. We thus propose a novel two-view transductive SVM that takes advantage of both the abundant amount of unlabeled data and their multiple representations to improve the performance of classifiers. The idea is fairly simple: train a classifier on each of the two views of both labeled and unlabeled data, and impose a global constraint that each classifier assigns the same class label to each labeled and unlabeled data. We applied our two-view transductive SVM to the WebKB course dataset, and a real-life review spam classification dataset. Experimental results show that our proposed approach performs up to 5% better than a single view learning algorithm, especially when the amount of labeled data is small. The other advantage of our two-view approach is its significantly improved stability, which is especially useful for noisy real world data.

Discipline

Computer Sciences | Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

2010 SIAM International Conference on Data Mining: April 29-May 1, Columbus, OH: Proceedings

First Page

235

Last Page

244

ISBN

9780898717037

Identifier

10.1137/1.9781611972801.21

Publisher

SIAM

City or Country

Philadelphia, PA

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1137/1.9781611972801.21

Download

Share

COinS