CLSU (Consensus Learning on Supervised and Unsupervised Models)

CLSU (Consensus Learning on Supervised and Unsupervised Models) is a framework designed to reach consensus among predictions obtained from heterogeneous information sources, either supervised or unsupervised.

• The paper and the presentation slides. (pdf) (ppt)
• Details of experiments on DBLP data sets.
  • Notes (txt).
  • Selected conferences (txt).
  • Results on selected authors: Task 1 (pdf), Task 2 (pdf).
• Datasets and codes. (zip)

Relevant publication:

Jing Gao, Wei Fan, Yizhou Sun, Jiawei Han, "Heterogeneous Source Consensus Learning via Decision Propagation and Negotiation". Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'09), Paris, France, June 2009, 339-347.

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maintained by Jing Gao (jinggao3 at illinois dot edu).