Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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. Hover, none of the current methods is able to assess the association beten feature combinations and a target variable while conditioning on a categorical covariate, in order to correct for potential confounding effects.  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 por on simulated and real-world datasets compared to the state of the art, opening the door to numerous applications in biomedicine.
['Laetitia Papaxanthos', 'Felipe Llinares-López', 'Dean A. Bodenham', 'Karsten M. Borgwardt']
I am writing a research paper on a idea Finding significant combinations of features in the presence of categorical covariates
[{"from": "human", "value": "I am writing a research paper on a idea Finding significant combinations of features in the presence of categorical covariates"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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. Hover, none of the current methods is able to assess the association beten feature combinations and a target variable while conditioning on a categorical covariate, in order to correct for potential confounding effects.  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 por on simulated and real-world datasets compared to the state of the art, opening the door to numerous applications in biomedicine."}]