Papers by Keyword: Auto-Associative Neural Network (AANN)

Abstract: Extreme learning machine (ELM), a relatively novel machine learning algorithm for single hidden layer feed-forward neural networks (SLFNs), has been shown competitive performance in simple structure and superior training speed. To improve the effectiveness of ELM for dealing with noisy datasets, a deep structure of ELM, short for DS-ELM, is proposed in this paper. DS-ELM contains three level networks (actually contains three nets ): the first level network is trained by auto-associative neural network (AANN) aim to filter out noise as well as reduce dimension when necessary; the second level network is another AANN net aim to fix the input weights and bias of ELM; and the last level network is ELM. Experiments on four noisy datasets are carried out to examine the new proposed DS-ELM algorithm. And the results show that DS-ELM has higher performance than ELM when dealing with noisy data.

Abstract: 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.