Detection of Cooperative Interactions in Logistic Regression Models
An important problem in the field of bioinformatics is to identify interactive effects among profiled variables for outcome prediction. In this paper, a logistic regression model with pairwise interactions among a set of binary covariates is considered. Modeling the structure of the interactions by a graph, our goal is to recover the interaction graph from independently identically distributed (i.i.d.) samples of the covariates and the outcome. When viewed as a feature selection problem, a simple quantity called influence is proposed as a measure of the marginal effects of the interaction terms on the outcome. For the case when the underlying interaction graph is known to be acyclic, it is shown that a simple algorithm that is based on a maximum-weight spanning tree with respect to the plug-in estimates of the influences not only has strong theoretical performance guarantees, but can also outperform generic feature selection algorithms for recovering the interaction graph from i.i.d. samples of the covariates and the outcome. Our results can also be extended to the model that includes both individual effects and pairwise interactions via the help of an auxiliary covariate.
Code (0)
등록된 구현이 없습니다.
Tasks
feature selectionregressionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
An Automatic Interaction Detection Hybrid Model for Bankcard Response Classification
In this paper, we propose a hybrid bankcard response model, which integrates decision tree based chi-square automatic interaction detection (CHAID) into logistic regression. In the first stage of the hybrid model, CHAID …
ClassificationGeneral ClassificationregressionValid auto-models for spatially autocorrelated occupancy and abundance data
Auto-logistic and related auto-models, implemented approximately as autocovariate regression, provide simple and direct modelling of spatial dependence. The autologistic model has been widely applied in ecology since Aug…
regressionvalidShapley Regression for Rare Disease Diagnosis Support: a case study on APDS
Activated PI3K8 Syndrome (APDS) is a rare genetic immune disorder caused by variants in PIK3CD or PIK3R1, with highly heterogeneous symptoms that often delay diagnosis. Early recognition is hampered by overlapping clinic…
Bayesian Hybrid Machine Learning of Gallstone Risk
Gallstone disease is a complex, multifactorial condition with significant global health burdens. Identifying underlying risk factors and their interactions is crucial for early diagnosis, targeted prevention, and effecti…
Decision MakingHybrid Machine LearningregressionVariable SelectionMachine Learning, Linear and Bayesian Models for Logistic Regression in Failure Detection Problems
In this work, we study the use of logistic regression in manufacturing failures detection. As a data set for the analysis, we used the data from Kaggle competition Bosch Production Line Performance. We considered the use…
BIG-bench Machine LearningGeneral Classificationregression