An information geometric approach to survival analysis and feature selection by neural networks

Antonio Eleuteri, Roberta Tagliaferri, Leopoldo Milano, Michele De Laurentiis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper an information geometric approach to survival analysis is described. It is shown how a neural network can be used to model the probability of failure of a system, and how it can be trained by minimising a suitable divergence functional in a Bayesian framework. By using the trained network, minimisation of the same divergence functional allows for fast, efficient and exact feature selection. Finally, the performance of the algorithms is illustrated on a synthetic dataset.

Original languageEnglish
Title of host publicationIEEE International Conference on Neural Networks - Conference Proceedings
Pages3229-3234
Number of pages6
Volume4
Publication statusPublished - 2004
Event2004 IEEE International Joint Conference on Neural Networks - Proceedings - Budapest, Hungary
Duration: Jul 25 2004Jul 29 2004

Other

Other2004 IEEE International Joint Conference on Neural Networks - Proceedings
CountryHungary
CityBudapest
Period7/25/047/29/04

ASJC Scopus subject areas

  • Software

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  • Cite this

    Eleuteri, A., Tagliaferri, R., Milano, L., & De Laurentiis, M. (2004). An information geometric approach to survival analysis and feature selection by neural networks. In IEEE International Conference on Neural Networks - Conference Proceedings (Vol. 4, pp. 3229-3234)