An important step in the analysis of high-dimensional biomedical data is feature selection. Typically, a feature subset selected by a feature selection method is evaluated for relevance towards a task such as prediction or classification. Another important property of a feature selection method is stability that refers to robustness of the selected features to perturbations in the data. In biom...

In the context of feature selection, there is a trade-off between the number of selected features and the generalisation error. Two plots may help to summarise feature selection: the feature selection path and the sparsity-error trade-off curve. The feature selection path shows the best feature subset for each subset size, whereas the sparsity-error trade-off curve shows the corresponding gener...

We report on the successful application of feature selection methods to a classification problem in molecular biology involving only 72 data points in a 7130 dimensional space. Our approach is a hybrid of filter and wrapper approaches to feature selection. We make use of a sequence of simple filters, culminating in Koller and Sahami’s (1996) Markov Blanket filter, to decide on particular featur...