A Comparison of State-of-the-Art Classification Techniques with Application to Cytogenetics

Boaz Lerner; Neil D. Lawrence

edit

Back to publications

A Comparison of State-of-the-Art Classification Techniques with Application to Cytogenetics

Boaz Lerner, Neil D. Lawrence

Neural Computing and Applications, 10(1):39-47, 2001.

Abstract

Several state-of-the-art techniques: a neural network, Bayesian neural network, support vector machine and naive Bayesian classifier are experimentally evaluated in discriminating fluorescence in-situ hybridization (FISH) signals. Highly accurate classification of signals from real data and artefacts of two cytogenetic probes (colours) is required for detecting abnormalities in the data. More than 3,100 FISH signals are classified by the techniques into colour and as real or artefact with accuracies of around 98% and 88%, respectively. The results of the comparison also show a trade-off between simplicity represented by the naive Bayesian classifier and high classification performance represented by the other techniques.

Links

Cite this Paper

BibTeX


@Article{Lerner-comparison01,
  title = 	 {A Comparison of State-of-the-Art Classification Techniques with Application to Cytogenetics},
  author = 	 {Lerner, Boaz and Lawrence, Neil D.},
  journal =      {Neural Computing and Applications},
  pages = 	 {39--47},
  year = 	 {2001},
  volume = 	 {10},
  number =       {1},
  pdf = 	 {https://lawrennd.github.io/publications/files/comparison.pdf},
  url = 	 {/publications/lerner-comparison01.html},
  abstract = 	 {Several state-of-the-art techniques: a neural network, Bayesian neural network, support vector machine and naive Bayesian classifier are experimentally evaluated in discriminating fluorescence in-situ hybridization (FISH) signals. Highly accurate classification of signals from real data and artefacts of two cytogenetic probes (colours) is required for detecting abnormalities in the data. More than 3,100 FISH signals are classified by the techniques into colour and as real or artefact with accuracies of around 98% and 88%, respectively. The results of the comparison also show a trade-off between simplicity represented by the naive Bayesian classifier and high classification performance represented by the other techniques.}
}

Endnote

%0 Journal Article
%T A Comparison of State-of-the-Art Classification Techniques with Application to Cytogenetics
%A Boaz Lerner
%A Neil D. Lawrence
%J Neural Computing and Applications
%D 2001	
%F Lerner-comparison01
%P 39--47
%U /publications/lerner-comparison01.html
%V 10
%N 1
%X Several state-of-the-art techniques: a neural network, Bayesian neural network, support vector machine and naive Bayesian classifier are experimentally evaluated in discriminating fluorescence in-situ hybridization (FISH) signals. Highly accurate classification of signals from real data and artefacts of two cytogenetic probes (colours) is required for detecting abnormalities in the data. More than 3,100 FISH signals are classified by the techniques into colour and as real or artefact with accuracies of around 98% and 88%, respectively. The results of the comparison also show a trade-off between simplicity represented by the naive Bayesian classifier and high classification performance represented by the other techniques.

RIS


TY  - JOUR
TI  - A Comparison of State-of-the-Art Classification Techniques with Application to Cytogenetics
AU  - Boaz Lerner
AU  - Neil D. Lawrence
DA  - 2001/04/01	
ID  - Lerner-comparison01
VL  - 10
IS  - 1
SP  - 39
EP  - 47
L1  - https://lawrennd.github.io/publications/files/comparison.pdf
UR  - /publications/lerner-comparison01.html
AB  - Several state-of-the-art techniques: a neural network, Bayesian neural network, support vector machine and naive Bayesian classifier are experimentally evaluated in discriminating fluorescence in-situ hybridization (FISH) signals. Highly accurate classification of signals from real data and artefacts of two cytogenetic probes (colours) is required for detecting abnormalities in the data. More than 3,100 FISH signals are classified by the techniques into colour and as real or artefact with accuracies of around 98% and 88%, respectively. The results of the comparison also show a trade-off between simplicity represented by the naive Bayesian classifier and high classification performance represented by the other techniques.
ER  -

APA


Lerner, B. & Lawrence, N.D.. (2001). A Comparison of State-of-the-Art Classification Techniques with Application to Cytogenetics. Neural Computing and Applications 10(1):39-47 Available from /publications/lerner-comparison01.html.