Interpreting support vector machine models for multivariate group wise analysis in neuroimaging

By Bilwaj Gaonkar, Russell T. Shinohara, Christos Davatzikos. 2015

67 citations recorded by the source corpus.

Machine learning based classification algorithms like support vector machines (SVMs) have shown great promise for turning a high dimensional neuroimaging data into clinically useful decision criteria. However, tracing imaging based patterns that contribute significantly to classifier decisions remains an open problem. This is an issue of critical importance in imaging studies seeking to determine which anatomical or physiological imaging features contribute to the classifier’s decision, thereby allowing users to critically evaluate the findings of such machine learning methods and to understand disease mechanisms. The majority of published work addresses the question of statistical inference for support vector classification using permutation tests based on SVM weight vectors. Such permutation testing ignores the SVM margin, which is critical in SVM theory. In this work we emphasize the use of a statistic that explicitly accounts for the SVM margin and show that the null distributions associated with this statistic are asymptotically normal. Further, our experiments show that this statistic is a lot less conservative as compared to weight based permutation tests and yet specific enough to tease out multivariate patterns in the data. Thus, we can better understand the multivariate patterns that the SVM uses for neuroimaging based classification.

Subfields: Artificial Intelligence, Molecular Biology, Statistics and Probability, Medical Imaging, Neuroscience

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Citations in the corpus

Explainable artificial intelligence: a comprehensive review (2021, background): “Four XAI reviews were published in 2020. Among them, two research papers were dedicated to reviewing the main XAI approaches in specific fields. Guo (2020) concentrated on summarizing XAI for the 6G field, whereas Tjoa and Guan (2020) discussed the recent XAI approaches for the medical. The two rema”
Human-in-the-loop machine learning: a state of the art (2022, methodology): “For SVMs, post-hoc explainability techniques applied are explanation by simplification, local explanations, visualizations and explanations by example (Barakat and Bradley 2007; Chen et al. 2007; Gaonkar et al. 2015).”