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  Finding significant combinations of features in the presence of categorical covariates

Papaxanthos, L., Llinares-López, F., Bodenham, D., & Borgwardt, K. (2016). Finding significant combinations of features in the presence of categorical covariates. Advances in Neural Information Processing Systems 29 (NIPS 2016), 2271-2279.

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Papaxanthos, Laetitia, Author
Llinares-López, Felipe, Author
Bodenham, Dean, Author
Borgwardt, Karsten1, Author                 
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1ETH Zürich, ou_persistent22              

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 Abstract: In high-dimensional settings, where the number of features p is typically much larger than the number of samples n, methods which can systematically examine arbitrary combinations of features, a huge 2^p-dimensional space, have recently begun to be explored. However, none of the current methods is able to assess the association between feature combinations and a target variable while conditioning on a categorical covariate, in order to correct for potential confounding effects. We propose the Fast Automatic Conditional Search (FACS) algorithm, a significant discriminative itemset mining method which conditions on categorical covariates and only scales as O(k log k), where k is the number of states of the categorical covariate. Based on the Cochran-Mantel-Haenszel Test, FACS demonstrates superior speed and statistical power on simulated and real-world datasets compared to the state of the art, opening the door to numerous applications in biomedicine.

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 Dates: 20162016
 Publication Status: Issued
 Pages: 2271-​2279
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Title: Advances in Neural Information Processing Systems 29 (NIPS 2016)
Source Genre: Journal
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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 2271 - 2279 Identifier: -