About: COMPARATIVE STUDY OF MACHINE LEARNING TECHNIQUES FOR SUPERVISED CLASSIFICATION OF BIOMEDICAL DATA

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An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

Attributes	Values
rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
rdfs:seeAlso	http://www.aei.tuke.sk/papers/2014/3/01_Drotar.pdf
Description	Several classification methods have been widely used in literature for identification of diseases or differential diagnosis of various types of disorders. Classification methods such as support vector machines, random forests, AdaBoost, deep belief networks, K- nearest neighbors, linear discriminant analysis or perceptron are probably the most popular ones. Even if these methods are frequently used there is a lack of comparison between them to find better framework for classification. In this study, we compared performance of the above mentioned classification methods. The 10-fold cross validation was used to calculate accuracy and Matthews correlation coefficient of the classifiers. In each case these methods were applied to eight binary biomedical datasets. The same evaluation was realized also in conjunction with feature selection technique that passed only hundred most relevant features. Even though there is no single classification method that dominates in terms of performance, we found that some Several classification methods have been widely used in literature for identification of diseases or differential diagnosis of various types of disorders. Classification methods such as support vector machines, random forests, AdaBoost, deep belief networks, K- nearest neighbors, linear discriminant analysis or perceptron are probably the most popular ones. Even if these methods are frequently used there is a lack of comparison between them to find better framework for classification. In this study, we compared performance of the above mentioned classification methods. The 10-fold cross validation was used to calculate accuracy and Matthews correlation coefficient of the classifiers. In each case these methods were applied to eight binary biomedical datasets. The same evaluation was realized also in conjunction with feature selection technique that passed only hundred most relevant features. Even though there is no single classification method that dominates in terms of performance, we found that some (en)
Title	COMPARATIVE STUDY OF MACHINE LEARNING TECHNIQUES FOR SUPERVISED CLASSIFICATION OF BIOMEDICAL DATA COMPARATIVE STUDY OF MACHINE LEARNING TECHNIQUES FOR SUPERVISED CLASSIFICATION OF BIOMEDICAL DATA (en)
skos:prefLabel	COMPARATIVE STUDY OF MACHINE LEARNING TECHNIQUES FOR SUPERVISED CLASSIFICATION OF BIOMEDICAL DATA COMPARATIVE STUDY OF MACHINE LEARNING TECHNIQUES FOR SUPERVISED CLASSIFICATION OF BIOMEDICAL DATA (en)
skos:notation	RIV/00216305:26220/14:PU112359!RIV15-MSM-26220___
http://linked.open...avai/riv/aktivita	P
http://linked.open...avai/riv/aktivity	P(ED2.1.00/03.0072)
http://linked.open...iv/cisloPeriodika	3
http://linked.open...vai/riv/dodaniDat	2015
http://linked.open...aciTvurceVysledku	Smékal, Zdeněk Drotár, Peter
http://linked.open.../riv/druhVysledku	J - Článek v odborném periodiku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Vysoké učení technické v Brně / Fakulta elektrotechniky a komunikačních technologií
http://linked.open...dnocenehoVysledku	7962
http://linked.open...ai/riv/idVysledku	RIV/00216305:26220/14:PU112359
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	SVM, AdaBoost, Random Forests, Deep Belief Networks, bioinformatics, microarray (en)
http://linked.open.../riv/klicoveSlovo	AdaBoost SVM bioinformatics microarray Random Forests Deep Belief Networks
http://linked.open...odStatuVydavatele	SK - Slovenská republika
http://linked.open...ontrolniKodProRIV	[D9C7487289B2]
http://linked.open...i/riv/nazevZdroje	Acta Electrotechnica et Informatica
http://linked.open...in/vavai/riv/obor	JA
http://linked.open...ichTvurcuVysledku	2 (xsd:int)
http://linked.open...cetTvurcuVysledku	2 (xsd:int)
http://linked.open...vavai/riv/projekt	Centre of sensor, information and communication systems
http://linked.open...UplatneniVysledku	2014
http://linked.open...v/svazekPeriodika	2014
http://linked.open...iv/tvurceVysledku	Smékal, Zdeněk Drotár, Peter
issn	1335-8243
number of pages	6 (xsd:int)
http://bibframe.org/vocab/doi	10.15546/aeei-2014-0021
http://localhost/t...ganizacniJednotka	26220

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