Name
Abstract | ||
|---|---|---|
This paper introduces a privacy-aware Bayesian approach that combines ensembles of classifiers and clusterers to perform semi-supervised and transductive learning. We consider scenarios where instances and their classification/clustering results are distributed across different data sites and have sharing restrictions. As a special case, the privacy aware computation of the model when instances of the target data are distributed across different data sites, is also discussed. Experimental results show that the proposed approach can provide good classification accuracies while adhering to the data/model sharing constraints. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/PASSAT/SocialCom.2011.172 | PASSAT) and 2011 IEEE Third Inernational Conference Social Computing |
Keywords | DocType | Volume |
pattern clustering,semisupervised learning,clusterer ensemble,data privacy,privacy-aware computation,classifier ensemble,learning (artificial intelligence),pattern classification,privacy aware bayesian approach,probabilistic model,transductive learning,data sharing constraint,model sharing constraint,classification accuracy,cluster ensemble,privacy aware computation,distributed processing,data sites,estimation,bayesian approach,data model,distributed databases,learning artificial intelligence,clustering algorithms,data models,distributed database,privacy,servers | Journal | abs/1204.4521 |
ISBN | Citations | PageRank |
978-1-4577-1931-8 | 0 | 0.34 |
References | Authors | |
13 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ayan Acharya | 1 | 109 | 12.35 |
| Eduardo R. Hruschka | 2 | 724 | 45.52 |
| Joydeep Ghosh | 3 | 7041 | 462.41 |