Rondina, JM;
Shawe-Taylor, J;
Mourão-Miranda, J;
(2012)
A new feature selection method based on stability theory - Exploring parameters space to evaluate classification accuracy in neuroimaging data.
In:
Machine Learning and Interpretation in Neuroimaging.
(pp. pp. 51-59).
Springer: Cham, Switzerland.
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Abstract
Recently we proposed a feature selection method based on stability theory. In the present work we present an evaluation of its performance in different contexts through a grid search performed in a subset of its parameters space. The main contributions of this work are: we show that the method can improve the classification accuracy in relation to the wholebrain in different functional datasets; we evaluate the parameters influence in the results, getting some insight in reasonable ranges of values; and we show that combinations of parameters that yield the best accuracies are stable (i.e., they have low rates of false positive selections).
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