Document Set Expansion with Positive-Unlabeled Learning: A Density
  Estimation-based Approach
Haiyang Zhang Qiuyi Chen Yuanjie Zou Yushan Pan Jia Wang Mark Stevenson
Document set expansion aims to identify relevant documents from a large
collection based on a small set of documents that are on a fine-grained topic.
Previous work shows that PU learning is a promising method for this task.
However, some serious issues remain unresolved, i.e. typical challenges that PU
methods suffer such as unknown class prior and imbalanced data, and the need
for transductive experimental settings. In this paper, we propose a novel PU
learning framework based on density estimation, called puDE, that can handle
the above issues. The advantage of puDE is that it neither constrained to the
SCAR assumption and nor require any class prior knowledge. We demonstrate the
effectiveness of the proposed method using a series of real-world datasets and
conclude that our method is a better alternative for the DSE task.