Technical reports

A comparison of state-of-the-art classification techniques with application to cytogenetics

Boaz Lerner, Neil D. Lawrence

October 1999, 34 pages

DOI: 10.48456/tr-475

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 flourescence in-situ hybridization (FISH) signals. Highly-accurate classification of signals from real data and artifacts of two cytogenetic probes (colours) is required for detecting abnormalities in the data. More than 3100 FISH signals are classified by the techniques into colour and as real or artifact 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.

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BibTeX record

@TechReport{UCAM-CL-TR-475,
  author =	 {Lerner, Boaz and Lawrence, Neil D.},
  title = 	 {{A comparison of state-of-the-art classification techniques
         	   with application to cytogenetics}},
  year = 	 1999,
  month = 	 oct,
  url = 	 {https://www.cl.cam.ac.uk/techreports/UCAM-CL-TR-475.pdf},
  institution =  {University of Cambridge, Computer Laboratory},
  doi = 	 {10.48456/tr-475},
  number = 	 {UCAM-CL-TR-475}
}