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

Boaz LernerNeil 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.

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}, url = {http://inverseprobability.com/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 http://inverseprobability.com/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 UR - http://inverseprobability.com/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 http://inverseprobability.com/publications/lerner-comparison01.html.

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