Making large-scale support vector machine learning practical
Advances in kernel methods
Building Text Classifiers Using Positive and Unlabeled Examples
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
A support vector method for multivariate performance measures
ICML '05 Proceedings of the 22nd international conference on Machine learning
Learning from positive and unlabeled examples
Theoretical Computer Science - Algorithmic learning theory (ALT 2000)
Substring selection for biomedical document classification
Bioinformatics
Learning classifiers from only positive and unlabeled data
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Finding Transport Proteins in a General Protein Database
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
ISMB/ECCB'09 Proceedings of the 2009 workshop of the BioLink Special Interest Group, international conference on Linking Literature, Information, and Knowledge for Biology
Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries
Proceedings of the 7th ACM international conference on Web search and data mining
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Classifiers are traditionally learned using sets of positive and negative training examples. However, often a classifier is required, but for training only an incomplete set of positive examples and a set of unlabeled examples are available. This is the situation, for example, with the Transport Classification Database (TCDB, www.tcdb.org), a repository of information about proteins involved in transmembrane transport. This paper presents and evaluates a method for learning to rank the likely relevance to TCDB of newly published scientific articles, using the articles currently referenced in TCDB as positive training examples. The new method has succeeded in identifying 964 new articles relevant to TCDB in fewer than six months, which is a major practical success. From a general data mining perspective, the contributions of this paper are (i) evaluating two novel approaches that solve the positive-only problem effectively, (ii) applying support vector machines in a state-of-the-art way for recognizing and ranking relevance, and (iii) deploying a system to update a widely-used, real-world biomedical database. Supplementary information including all data sets are publicly available at www.cs.ucsd.edu/users/knoto/pub/ajcai08.