Forecasting using rules extracted from privacy preservation neural network

Authors:
Nekuri Naveen;Vadlamani Ravi;Chillarige Raghavendra Rao
Affiliations:
Institute for Development and Research in banking Technology, Hyderabad, AP, India;Institute for Development and Research in banking Technology, Hyderabad, AP, India;Department of Computer & Information Sciences, University of Hyderabad, Hyderabad, AP, India
Venue:
MIWAI'11 Proceedings of the 5th international conference on Multi-Disciplinary Trends in Artificial Intelligence
Year:
2011

Citing 10
Cited 0

Privacy Preserving Data Mining

CRYPTO '00 Proceedings of the 20th Annual International Cryptology Conference on Advances in Cryptology
Cryptographic techniques for privacy-preserving data mining

ACM SIGKDD Explorations Newsletter
Web Data Mining & Business Intelligence Analysis

Web Data Mining & Business Intelligence Analysis
An introduction to variable and feature selection

The Journal of Machine Learning Research
On Privacy-Preserving Access to Distributed Heterogeneous Healthcare Information

HICSS '04 Proceedings of the Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS'04) - Track 6 - Volume 6
A Framework for Evaluating Privacy Preserving Data Mining Algorithms*

Data Mining and Knowledge Discovery
Privacy preservation in data mining using hybrid perturbation methods: an application to bankruptcy prediction in banks

International Journal of Data Analysis Techniques and Strategies
Privacy preserving Back-propagation neural network learning over arbitrarily partitioned data

Neural Computing and Applications
Confidentiality issues for medical data miners

Artificial Intelligence in Medicine
DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction

IEEE Transactions on Fuzzy Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Privacy preserving data mining is of paramount importance in many areas. In this paper, we employ Particle Swarm Optimization (PSO) trained Auto Associative Neural Network (PSOAANN) for preservation privacy in input feature values. The privacy preserved input features are fed to the Dynamic Evolving Neuro Fuzzy Inference System (DENFIS) and Classification and Regression Tree (CART) separately for rule extraction purpose. We also propose a new feature selection method using PSOAANN. Thus, in this study, PSOAANN accomplishes privacy preservation as well as feature selection. The performance of the hybrid is tested using 10 fold cross validation on 5 regression datasets viz. Auto MPG , Body Fat , Boston Housing , Forest Fires and Pollution . The study demonstrates the effectiveness of the proposed approach in generating accurate regression rules with and without feature selection. The ttest at 1% level of significance is performed to see whether the difference in results obtained in the case of with and without feature selection is statistically significant or not. In the case of PSOAANN + CART, it is observed that the result is statistical insignificant between with and without feature selection in four datasets. In the case of PSOAANN + DENFIS, it is observed that statistical significance between with and without feature selection for three datasets. Hence, from the t-test it is concluded that the proposed feature selection method yielded better or comparable results.