A framework for modeling positive class expansion with single snapshot

Abstract

In many real-world data mining tasks, the coverage of the target concept may change as the time changes. For example,the coverage of "learned knowledge" of a student today may be different from his/er "learned knowledge" tomorrow, since the "learned knowledge" of the student is in expanding everyday. In order to learn a model capable of making accurate predictions, the evolution of the concept must be considered, and thus, a series of data sets collected at different time is needed. However, in many cases there is only a single data set instead of a series of data sets. In other words, only a single snapshot of the data along the time axis is available. In this paper, we show that for positive class expansion, i.e., the coverage of the target concept is in expanding as illustrated in the above "learned knowledge" example, we can learn an accurate model from the single snapshot data with the help of domain knowledge given by user. The effectiveness of the proposed framework is validated in experiments. © 2008 Springer-Verlag Berlin Heidelberg.