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A new feature selection method based on stability theory - Exploring parameters space to evaluate classification accuracy in neuroimaging data

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. Green open access

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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).

Type: Proceedings paper
Title: A new feature selection method based on stability theory - Exploring parameters space to evaluate classification accuracy in neuroimaging data
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-642-34713-9_7
Publisher version: http://dx.doi.org/10.1007/978-3-642-34713-9_7
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Support Vector Machine, Feature Selection, Recursive Feature Elimination, Stability Selection, Placebo Analgesia
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Brain Repair and Rehabilitation
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10098450
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