Context-dependent feature analysis with random forests

2016/05/12 by Antonio Sutera, Sutera, Antonio, Gilles Louppe +7
Biochemistry, Genetics and Molecular Biology · Computer Science · #FOS: Computer and information sciences #Face and Expression Recognition #Gene expression and cancer classification #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications

paper · pdf · doi:10.48550/arxiv.1605.03848

openalex publication_date 2016/05/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In many cases, feature selection is often more complicated than identifying a single subset of input variables that would together explain the output. There may be interactions that depend on contextual information, i.e., variables that reveal to be relevant only in some specific circumstances. In this setting, the contribution of this paper is to extend the random forest variable importances framework in order (i) to identify variables whose relevance is context-dependent and (ii) to characterize as precisely as possible the effect of contextual information on these variables. The usage and the relevance of our framework for highlighting context-dependent variables is illustrated on both artificial and real datasets.

Citations

• Wrappers for feature subset selection

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