Detail publikace

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

DROTÁR, P. SMÉKAL, Z.

Originální název

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

Typ

článek v časopise - ostatní, Jost

Jazyk

angličtina

Originální abstrakt

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 methods provide more consistent performance than others.

Klíčová slova

SVM, AdaBoost, Random Forests, Deep Belief Networks, bioinformatics, microarray

Autoři

DROTÁR, P.; SMÉKAL, Z.

Rok RIV

2014

Vydáno

30. 9. 2014

Nakladatel

Technical University of Kosice

Místo

Kosice

ISSN

1335-8243

Periodikum

Acta Electrotechnica et Informatica

Ročník

2014

Číslo

3

Stát

Slovenská republika

Strany od

5

Strany do

10

Strany počet

6

URL

BibTex

@article{BUT112064,
  author="Peter {Drotár} and Zdeněk {Smékal}",
  title="COMPARATIVE STUDY OF MACHINE LEARNING TECHNIQUES FOR SUPERVISED CLASSIFICATION OF BIOMEDICAL DATA",
  journal="Acta Electrotechnica et Informatica",
  year="2014",
  volume="2014",
  number="3",
  pages="5--10",
  doi="10.15546/aeei-2014-0021",
  issn="1335-8243",
  url="http://www.aei.tuke.sk/papers/2014/3/01_Drotar.pdf"
}