Large-scale feature selection using evolved neural networks

Stathakis, D; Topouzelis, K; Karathanassi, V

dc.contributor.author	Stathakis, D	en
dc.contributor.author	Topouzelis, K	en
dc.contributor.author	Karathanassi, V	en
dc.date.accessioned	2014-03-01T02:50:26Z
dc.date.available	2014-03-01T02:50:26Z
dc.date.issued	2006	en
dc.identifier.issn	0277786X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/35116
dc.subject	Feature selection	en
dc.subject	Genetic algorithm	en
dc.subject	Neural networks	en
dc.subject	Oil spill	en
dc.subject	SAR	en
dc.subject.other	Dark formations	en
dc.subject.other	Large scale feature selection	en
dc.subject.other	Neural network topology	en
dc.subject.other	Testing classification accuracy	en
dc.subject.other	Artificial intelligence	en
dc.subject.other	Classification (of information)	en
dc.subject.other	Genetic algorithms	en
dc.subject.other	Large scale systems	en
dc.subject.other	Neural networks	en
dc.subject.other	Oil bearing formations	en
dc.subject.other	Synthetic aperture radar	en
dc.subject.other	Feature extraction	en
dc.title	Large-scale feature selection using evolved neural networks	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1117/12.688149	en
heal.identifier.secondary	http://dx.doi.org/10.1117/12.688149	en
heal.identifier.secondary	636513	en
heal.publicationDate	2006	en
heal.abstract	In this paper computational intelligence, referring here to the synergy of neural networks and genetic algorithms, is deployed in order to determine a near-optimal neural network for the classification of dark formations in oil spills and look-alikes. Optimality is sought in the framework of a multi-objective problem, i.e. the minimization of input features used and, at the same time, the maximization of overall testing classification accuracy. The proposed method consists of two concurrent actions. The first is the identification of the subset of features that results in the highest classification accuracy on the testing data set i.e. feature selection. The second parallel process is the search for the neural network topology, in terms of number of nodes in the hidden layer, which is able to yield optimal results with respect to the selected subset of features. The results show that the proposed method, i.e. concurrently evolving features and neural network topology, yields superior classification accuracy compared to sequential floating forward selection as well as to using all features together. The accuracy matrix is deployed to show the generalization capacity of the discovered neural network topology on the evolved sub-set of features.	en
heal.journalName	Proceedings of SPIE - The International Society for Optical Engineering	en
dc.identifier.doi	10.1117/12.688149	en
dc.identifier.volume	6365	en