Random Subspace Ensemble Artificial Neural Networks for Firstepisode Schizophrenia Classification
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14110%2F16%3A00091907" target="_blank" >RIV/00216224:14110/16:00091907 - isvavai.cz</a>
Alternative codes found
RIV/65269705:_____/16:00065861
Result on the web
<a href="http://dx.doi.org/10.15439/2016F333" target="_blank" >http://dx.doi.org/10.15439/2016F333</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.15439/2016F333" target="_blank" >10.15439/2016F333</a>
Alternative languages
Result language
angličtina
Original language name
Random Subspace Ensemble Artificial Neural Networks for Firstepisode Schizophrenia Classification
Original language description
Computer-aided schizophrenia diagnosis is a difficult task that has been developing for last decades. Since traditional classifiers have not reached sufficient sensitivity and specificity, another possible way is combining the classifiers in ensembles. In this paper, we take advantage of random subspace ensemble method and combine it with multilayer perceptron (MLP) and support vector machines (SVM). Our experiment employs voxel-based morphometry to extract the grey matter densities from 52 images of first-episode schizophrenia patients and 52 healthy controls. MLP and SVM are adapted on random feature vectors taken from predefined feature pool and the classification results are based on their voting. Random feature ensemble method improved prediction of schizophrenia when short input feature vector (100 features) was used, however the performance was comparable with single classifiers based on bigger input feature vector (1000 and 10000 features).
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Annals of Computer Science and Information Systems, Volume 8 : Proceedings of the 2016 Federated Conference on Computer Science and Information Systems
ISBN
9788360810903
ISSN
2300-5963
e-ISSN
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Number of pages
5
Pages from-to
317-321
Publisher name
Polskie Towarzystwo Informatyczne; Institute of Electrical and Electronics Engineers
Place of publication
Warzaw; Los Alamitos
Event location
Gdańsk
Event date
Sep 11, 2016
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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