ArticleNeuroinformatics2019
Sign-Consistency Based Variable Importance for Machine Learning in Brain Imaging.
Article in Neuroinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Machine-learning classification of documented pedophilic sexual interest from eye-tracking patterns.Research square · 2026Article
- Structural imaging predictors of ketamine response in treatment-resistant depression: a machine learning approach.Translational psychiatry · 2026Article
- Brain ventricle morphology markers in predicting shunt surgery outcome in idiopathic normal-pressure hydrocephalus.Fluids and barriers of the CNS · 2026Article
- Ensuring generalizability and clinical utility in mental health care applications: Robust artificial intelligence-based treatment predictions in diverse psychosis populations.Psychiatry and clinical neurosciences · 2026Article
- Multimodal biological profiles of symptom-based subgroups in recent-onset psychosis.Research square · 2025Article
- EEG-based Signatures of Schizophrenia, Depression, and Aberrant Aging: A Supervised Machine Learning Investigation.Schizophrenia bulletin · 2025Article
- Exploring the predictive value of structural covariance networks for the diagnosis of schizophrenia.Frontiers in psychiatry · 2025Article
- The BMIgap tool to quantify transdiagnostic brain signatures of current and future weight.Nature. Mental health · 2025Article
- Multivariable prediction of functional outcome after first-episode psychosis: a crossover validation approach in EUFEST and PSYSCAN.Schizophrenia (Heidelberg, Germany) · 2024Article
- Classifying autism in a clinical population based on motion synchrony: a proof-of-concept study using real-life diagnostic interviews.Scientific reports · 2024Article
- Psychosis brain subtypes validated in first-episode cohorts and related to illness remission: results from the PHENOM consortium.Molecular psychiatry · 2023Article
- Patterns of risk-Using machine learning and structural neuroimaging to identify pedophilic offenders.Frontiers in psychiatry · 2023Article
- Neural Correlates of Smooth Pursuit Eye Movements in Schizotypy and Recent Onset Psychosis: A Multivariate Pattern Classification Approach.Schizophrenia bulletin open · 2022Article
- Deep Mining from Omics Data.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Regularized Bagged Canonical Component Analysis for Multiclass Learning in Brain Imaging.Neuroinformatics · 2020Article
Corrections and comments
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Authors and funding
4 authors.
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Abstract
An important problem that hinders the use of supervised classification algorithms for brain imaging is that the number of variables per single subject far exceeds the number of training subjects available. Deriving multivariate measures of variable importance becomes a challenge in such scenarios. This paper proposes a new measure of variable importance termed sign-consistency bagging (SCB). The SCB captures variable importance by analyzing the sign consistency of the corresponding weights in an ensemble of linear support vector machine (SVM) classifiers. Further, the SCB variable importances are enhanced by means of transductive conformal analysis. This extra step is important when the data can be assumed to be heterogeneous. Finally, the proposal of these SCB variable importance measures is completed with the derivation of a parametric hypothesis test of variable importance. The new importance measures were compared with a t-test based univariate and an SVM-based multivariate variable importances using anatomical and functional magnetic resonance imaging data. The obtained results demonstrated that the new SCB based importance measures were superior to the compared methods in terms of reproducibility and classification accuracy.
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