The effect of noise and biases on the performance of machine learning algorithms.
This paper describes the results of experiments with a machine learning algorithm for the induction of classification trees. We mainly address the impact of noise on the resulting classification tree and on the classification results obtained with the derived tree. We use the domain of the biochemical assessment of thyroid diseases as an example. Some suggestions for quality assessment are outlined that should be available in tools that assist users in deriving classification trees in noisy domains.
Talmon
J L
JL
University of Limburg, Department of Medical Informatics and Statistics, Maastricht, The Netherlands.
McNair
P
P
D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D015982:Bias; D002965:Classification; D003663:Decision Trees; D005069:Evaluation Studies as Topic; D006801:Humans; D011336:Probability; D015983:Selection Bias; D013960:Thyroid Function Tests