Predictive Modeling with Heterogeneous Sources

Lack of labeled training examples is a common problem for many applications. In the same time, there is usually an abundance of labeled data from related tasks. But they have different distributions and outputs (e.g., different class labels, and different scales of regression values). Conjecture that there is only a limited number of vaccine efficacy examples against the new epidemic swine flu H1N1, whereas there exists a large amount of labeled vaccine data against previous years' flu. However, it is difficult to directly apply the older flu vaccine data as training examples because of the difference in data distribution and efficacy output criteria between different viruses. To increase the sources of labeled data, we propose a method to utilize these examples whose marginal distribution and output criteria can be different. The idea is to first select a subset of source examples similar in distribution to the target data; all the selected instances are then "re-scaled&quo...

• Type: Conference
• Year: 2010
• Where: SDM
• Authors: Xiaoxiao Shi, Qi Liu, Wei Fan, Qiang Yang, Philip S. Yu
• Keywords: Data Mining, Labeled Training Examples, SDM 2010, Vaccine Data, Vaccine Efficacy Examples