Large Scale URL-based Classification using Online Incremental Learning
Nidhi Singh, Harsimrat Sandhawalia, Nicolas Monet, Jean-Marc Coursimault, Herve Poirier
We address the problem of large-scale topic classification
of web pages based on the minimal text available
in the URLs. This problem is challenging because of the
sparsity of feature vectors that are derived from the URL
text, and the typical asymmetry between the cardinality of
train and test sets due to non-availability of sufficient sets of
annotated URLs for training and very large test sets (e.g.,
in the case of large-scale focused crawling). We propose an
online incremental learning algorithm which addresses these
issues. Our experiments based on large publicly available
datasets demonstrate an improvement of 0.11–0.12 in terms of
F-measure over the baseline algorithms, like Support Vector
Machine, in difficult scenarios where the cardinality of train set is just a fraction of that of the test set.
The 11th International Conference on Machine Learning and Applications, Boca Raton, Florida, USA, December 12-15, 2012.