Rule Extraction from Privacy Preserving Neural Network: Application to Banking
| Periodical | Advanced Materials Research (Volumes 403 - 408) |
|---|---|
| Main Theme | MEMS, NANO and Smart Systems |
| Edited by | Li Yuan |
| Pages | 920-928 |
| DOI | 10.4028/www.scientific.net/AMR.403-408.920 |
| Citation | Nekuri Naveen et al., 2011, Advanced Materials Research, 403-408, 920 |
| Online since | November, 2011 |
| Authors | Nekuri Naveen, V. Ravi, C. Raghavendra Rao |
| Keywords | Auto-Associative Neural Network (AANN), Bankruptcy, Classification, Particle Swarm Optimization Algorithm (PSO), Particle Swarm Optimization Auto-Associative Neural Network (PSOAANN), Privacy Preservation, Rule Extraction from Privacy Preservation |
| Price | US$ 28,- |
In the last two decades in areas like banking, finance and medical research privacy policies restrict the data owners to share the data for data mining purpose. This issue throws up a new area of research namely privacy preserving data mining. In this paper, we proposed a privacy preservation method by employing Particle Swarm Optimization (PSO) trained Auto Associative Neural Network (PSOAANN). The modified (privacy preserved) input values are fed to a decision tree (DT) and a rule induction algorithm viz., Ripper for rule extraction purpose. The performance of the hybrid is tested on four benchmark and bankruptcy datasets using 10-fold cross validation. The results are compared with those obtained using the original datasets where privacy is not preserved. The proposed hybrid approach achieved good results in all datasets.
