Real-life datasets in biomedicine often include missing values. When learning a Bayesian network classifier from such a dataset, the missing values are typically filled in by means of an imputation method to arrive at a complete dataset. The thus completed dataset then is used for the classifier's construction. When learning a selective classifier, also the selection of appropriate features is based upon the completed data. The resulting classifier, however, is likely to be used in the original real-life setting where it is again confronted with missing values. By means of a real-life dataset in the field of oesophageal cancer that includes a relatively large number of missing values, we argue that especially the wrapper approach to feature selection may result in classifiers that are too selective for such a setting and that, in fact, some redundancy is required to arrive at a reasonable classification accuracy in practice. © Springer-Verlag Berlin Heidelberg 2004.
CITATION STYLE
Blanco, R., Van Der Gaag, L. C., Inza, I., & Larrañaga, P. (2004). Selective classifiers can be too restrictive: A case-study in oesophageal cancer. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3337, 212–223. https://doi.org/10.1007/978-3-540-30547-7_22
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