R. Ghani. ICML 2002
Citation
R. Ghani. Combining Labeled and Unlabeled Data for MultiClass Text Categorization. In Proceedings of ICML, 2002.
Online version
Summary
This paper presents a new semi-supervised learning algorithm.
It decomposes multi-class classification problem into n binary ones using ECOC and Co-training is used for learning each individual binary classifier.
The performance of this algorithm relies on two assumptions:
- ECOC can outperform Naive Bayes on multi-class problem.
- Co-training can improve over a single Naive Bayes classifier using unlabeled data.
The joint effect of combining two methods together would improve the perform even further.
To evaluate this approach, the author compared it with Naive Bayes, EM and Co-training on two datasets Hoovers and Jobs.
Hoovers dataset that contains over 108,000 web pages of different companies. Since there are no natural feature split, the author randomly split the vocabulary into two halves and treat them as two separate feature sets.
Another dataset used for experiments is Jobs dataset. Job titles and job description are used separate feature sets for Co-training.
