Paper Info
Title
Finding significant combinations of features in the presence of categorical covariates.
Abstract
In high-dimensional settings, where the number of features p is much larger than the number of samples n, methods that systematically examine arbitrary combinations of features have only 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. As a result, many false discoveries might occur due to unaccounted 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.
Year
Venue
Field
2016
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 29 (NIPS 2016)
Covariate,Confounding,Pattern recognition,Computer science,Categorical variable,Artificial intelligence,Biomedicine,Statistics,Discriminative model,Statistical power
DocType
Volume
ISSN
Conference
29
1049-5258
Citations 
PageRank 
References 
2
0.36
0
Authors
4
Name
Order
Citations
PageRank
laetitia papaxanthos1102.01
Felipe Llinares-Lopez2112.76
Dean A. Bodenham3234.49
Karsten M. Borgwardt42799155.36