Learning from positive and unlabeled data

In some machine learning scenario, it's hard or unnatural to get negative training samples. For example, it's easy to find a collection of articles on some topic, but it may be hard to find a collection not on some topic. Another example is, in research community people tend to publish their positive results but not negative results. This leads to the research of one-class classification, which learns a classifier from positive data only. One such technique is one-class SVM. However, one-class classifiers are usually sensitive to parameters, and how to do parameter tuning on them is still largely an open problem (correct me if I'm wrong). You can't tune them by means like cross-validation because you don't have negative data!

So semi-supervised learning comes to rescue. Semi-supervised learning tries to learn a classifier from both labeled and unlabeled data, and unlabeled data is usually easy to get. There have been quite some methods on learning from positive and unlabeled data. A recent one is "Learning Classifiers from Only Positive and Unlabeled Data" (KDD 08). This paper makes the assumption of "selected completely at random", which says that whether a positive sample is labeled or not is independent of the data sample itself. From that they make some nice derivations and basically turn the problem into a traditional classification problem, which is simpler than many of the previous methods. The experimental results show that this method is as good as the best previous work, biased-SVM (which is also much less efficient due to parameter tuning).