A Comparison of State - of - the - ArtClassi cation Techniques withApplication

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