Classification of medical data with a robust multi-level combination scheme

Tsirogiannis, GL; Frossyniotis, D; Stoitsis, J; Golemati, S; Stafylopatis, A; Nikita, KS

dc.contributor.author	Tsirogiannis, GL	en
dc.contributor.author	Frossyniotis, D	en
dc.contributor.author	Stoitsis, J	en
dc.contributor.author	Golemati, S	en
dc.contributor.author	Stafylopatis, A	en
dc.contributor.author	Nikita, KS	en
dc.date.accessioned	2014-03-01T02:42:32Z
dc.date.available	2014-03-01T02:42:32Z
dc.date.issued	2004	en
dc.identifier.issn	10987576	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31034
dc.subject	Computer Aided Diagnosis	en
dc.subject	Medical Diagnosis	en
dc.subject	Support Vector Machine	en
dc.subject	Decision Tree	en
dc.subject	Neural Network	en
dc.subject.other	Decision trees	en
dc.subject.other	Intelligent classifiers	en
dc.subject.other	Multi-layered feed-forward neural networks	en
dc.subject.other	Support vector machines (SVM)	en
dc.subject.other	Algorithms	en
dc.subject.other	Decision theory	en
dc.subject.other	Error analysis	en
dc.subject.other	Feedforward neural networks	en
dc.subject.other	Multilayer neural networks	en
dc.subject.other	Probability	en
dc.subject.other	Reliability	en
dc.subject.other	Computer aided diagnosis	en
dc.title	Classification of medical data with a robust multi-level combination scheme	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/IJCNN.2004.1381020	en
heal.identifier.secondary	http://dx.doi.org/10.1109/IJCNN.2004.1381020	en
heal.publicationDate	2004	en
heal.abstract	Computer Aided Diagnosis is based on classification of medical data by intelligent classifiers. Especially for medical purposes, the classification must be very efficient, as diagnosis demands a high rate of reliability. Under most circumstances, single classifiers, such as Neural Networks, Support Vector Machines and Decision Trees, exhibit worse performance than ensemble combinations of them, as Bagging and Boosting are. In order to further enhance performance, we propose here a combination of these combination methods in a multi-level combination scheme. After experimentation by using four medical diagnosis problems, the proposed approach seems to be efficient in decreasing the error, compared to the best combining method standalone.	en
heal.journalName	IEEE International Conference on Neural Networks - Conference Proceedings	en
dc.identifier.doi	10.1109/IJCNN.2004.1381020	en
dc.identifier.volume	3	en
dc.identifier.spage	2483	en
dc.identifier.epage	2487	en