Random Subspace Ensemble Artificial Neural Networks for First-episode Schizophrenia Classification

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F65269705%3A_____%2F16%3A00065861" target="_blank" >RIV/65269705:_____/16:00065861 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216224:14110/16:00091907

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Random Subspace Ensemble Artificial Neural Networks for First-episode Schizophrenia Classification

Popis výsledku v původním jazyce

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).

Název v anglickém jazyce

Random Subspace Ensemble Artificial Neural Networks for First-episode Schizophrenia Classification

Popis výsledku anglicky

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).

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 2016 Federated Conference on Computer Science and Information Systems (FEDCSIS)

ISBN

978-83-60810-90-3

ISSN

2300-5963

e-ISSN

—

Počet stran výsledku

5

Strana od-do

317-321

Název nakladatele

IEEE

Místo vydání

New York

Místo konání akce

Gdansk, Poland

Datum konání akce

11. 9. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000392436600050