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@inproceedings{1361770,
author = {Vyškovský, Roman and Schwarz, Daniel and Janoušová, Eva and Kašpárek, Tomáš},
address = {Warzaw; Los Alamitos},
booktitle = {Annals of Computer Science and Information Systems, Volume 8 : Proceedings of the 2016 Federated Conference on Computer Science and Information Systems},
doi = {http://dx.doi.org/10.15439/2016F333},
editor = {Maria Ganzha, Leszek Maciaszek, Marcin Paprzycki},
keywords = {first-episode schizophrenia},
howpublished = {tištěná verze "print"},
language = {eng},
location = {Warzaw; Los Alamitos},
isbn = {978-83-60810-90-3},
pages = {317-321},
publisher = {Polskie Towarzystwo Informatyczne; Institute of Electrical and Electronics Engineers},
title = {Random Subspace Ensemble Artificial Neural Networks for Firstepisode Schizophrenia Classification},
url = {https://fedcsis.org/proceedings/2016/drp/333.html},
year = {2016}
}
TY - JOUR
ID - 1361770
AU - Vyškovský, Roman - Schwarz, Daniel - Janoušová, Eva - Kašpárek, Tomáš
PY - 2016
TI - Random Subspace Ensemble Artificial Neural Networks for Firstepisode Schizophrenia Classification
PB - Polskie Towarzystwo Informatyczne; Institute of Electrical and Electronics Engineers
CY - Warzaw; Los Alamitos
SN - 9788360810903
KW - first-episode schizophrenia
UR - https://fedcsis.org/proceedings/2016/drp/333.html
L2 - https://fedcsis.org/proceedings/2016/drp/333.html
N2 - 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).
ER -
VYŠKOVSKÝ, Roman, Daniel SCHWARZ, Eva JANOUŠOVÁ a Tomáš KAŠPÁREK. Random Subspace Ensemble Artificial Neural Networks for Firstepisode Schizophrenia Classification. In Maria Ganzha, Leszek Maciaszek, Marcin Paprzycki. \textit{Annals of Computer Science and Information Systems, Volume 8 : Proceedings of the 2016 Federated Conference on Computer Science and Information Systems}. Warzaw; Los Alamitos: Polskie Towarzystwo Informatyczne; Institute of Electrical and Electronics Engineers, 2016, s.~317-321. ISBN~978-83-60810-90-3. Dostupné z: https://dx.doi.org/10.15439/2016F333.
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