Relevant Feature Set Estimation with a Knock-out Strategy and Random Forests

By Melanie Ganz, Douglas N. Greve, Bruce Fischl, Ender Konukoglu. 2015

21 citations recorded by the source corpus.

Group analysis of neuroimaging data is a vital tool for identifying anatomical and functional variations related to diseases as well as normal biological processes. The analyses are often performed on a large number of highly correlated measurements using a relatively smaller number of samples. Despite the correlation structure, the most widely used approach is to analyze the data using univariate methods followed by post-hoc corrections that try to account for the data’s multivariate nature. Although widely used, this approach may fail to recover from the adverse effects of the initial analysis when local effects are not strong. Multivariate pattern analysis (MVPA) is a powerful alternative to the univariate approach for identifying relevant variations. Jointly analyzing all the measures, MVPA techniques can detect global effects even when individual local effects are too weak to detect with univariate analysis. Current approaches are successful in identifying variations that yield highly predictive and compact models. However, they suffer from lessened sensitivity and instabilities in identification of relevant variations. Furthermore, current methods’ user-defined parameters are often unintuitive and difficult to determine. In this article, we propose a novel MVPA method for group analysis of high-dimensional data that overcomes the drawbacks of the current techniques. Our approach explicitly aims to identify all relevant variations using a “knock-out” strategy and the Random Forest algorithm. In evaluations with synthetic datasets the proposed method achieved substantially higher sensitivity and accuracy than the state-of-the-art MVPA methods, and outperformed the univariate approach when the effect size is low. In experiments with real datasets the proposed method identified regions beyond the univariate approach, while other MVPA methods failed to replicate the univariate results. More importantly, in a reproducibility study with the well-known ADNI dataset the proposed method yielded higher stability and power than the univariate approach.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Visual Feature Attribution Using Wasserstein GANs (2017, methodology): “The most common approach is to use regression analysis or machine learning tools to generate population average maps, which highlights features that are salient across the population [3, 32, 61, 16, 39, 45, 14].”
Reconstructing subject‐specific effect maps (2017, unknown): “nt, maps indicate statistical relationship between the condition and measurement across the entire population. Either a group analysis technique [2,22,40] or a machine learning based predictive model [1,14,26,31,13] is used to compare two cohorts of subjects, one showing the condition of interest an”
Generative Discriminative Models for Multivariate Inference and Statistical Mapping in Medical Imaging (2018, methodology): “This results in improved sensitivity and numerous approaches have been proposed to efficiently obtain meaningful multivariate brain patterns [10, 14, 13, 4, 7, 6].”
Constructing Subject- and Disease-Specific Effect Maps: Application to Neurodegenerative Diseases (2016, methodology): “Predictive modeling approaches use machine-learning tools to identify measurements that are highly informative for diagnosing the condition [9,10,11,12].”