Learning to Find Relevant Biological Articles without Negative Training Examples

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) devising and evaluating two novel approaches that solve the positive-only problem effectively, (ii) applying support vector machines in a state-o...

• Type: Conference
• Year: 2008
• Where: AUSAI
• Authors: Keith Noto, Milton H. Saier Jr., Charles Elkan
• Keywords: Artificial Intelligence, AUSAI 2008, Negative Training Examples, Positive Training Examples, Training Examples