The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Random subspace method for multivariate feature selection
Pattern Recognition Letters
Computational Methods of Feature Selection (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series)
Stable feature selection via dense feature groups
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Robust Feature Selection Using Ensemble Feature Selection Techniques
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Clustered sampling improves random subspace brain mapping
Pattern Recognition
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Functional magnetic resonance imaging is a technology allowing for a non-invasive measurement of the brain activity. Data are encoded as sequences of 3D images, usually few hundreds samples, each made by tens of thousands voxels, namely volumetric pixels. The main question in neuroimaging is the identification of the voxels affected by a specific brain activity. This task, referred to as brain mapping, can be conceived as a problem of feature rating. The challenge is twofold: the former is to deal with the high feature space dimensionality; the latter is the need for preservation of redundant features. Most common techniques of feature selection do not cover both requirements. In this work we propose the adoption of a random subspace method, arguing, by theoretical arguments and empirical evidence on synthetic data, that it might be a viable solution for a multi-variate approach to brain mapping. In addition we provide some results on a neuroscientific case study investigating on a visual perception task.