PEBL: positive example based learning for Web page classification using SVM
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Building Text Classifiers Using Positive and Unlabeled Examples
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Label propagation through linear neighborhoods
ICML '06 Proceedings of the 23rd international conference on Machine learning
Substring selection for biomedical document classification
Bioinformatics
Simple, robust, scalable semi-supervised learning via expectation regularization
Proceedings of the 24th international conference on Machine learning
Regularized clustering for documents
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to Information Retrieval
Introduction to Information Retrieval
Learning to classify texts using positive and unlabeled data
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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Text classification is essential for narrowing down the number of documents relevant to a particular topic for further pursual, especially when searching through large biomedical databases. Protein-protein interactions are an example of such a topic with databases being devoted specifically to them. This paper proposed a semi-supervised learning algorithm via local learning with class priors (LL-CP) for biomedical text classification where unlabeled data points are classified in a vector space based on their proximity to labeled nodes. The algorithm has been evaluated on a corpus of biomedical documents to identify abstracts containing information about protein-protein interactions with promising results. Experimental results show that LL-CP outperforms the traditional semi-supervised learning algorithms such as SVM and it also performs better than local learning without incorporating class priors.