Variable selection using random forests

This paper proposes, focusing on random forests, the increasingly used statistical method for classification and regression problems introduced by Leo Breiman in 2001, to investigate two classical issues of variable selection. The first one is to find important variables for interpretation and the second one is more restrictive and try to design a good parsimonious prediction model. The main contribution is twofold: to provide some experimental insights about the behavior of the variable importance index based on random forests and to propose a strategy involving a ranking of explanatory variables using the random forests score of importance and a stepwise ascending variable introduction strategy.

• Publication year

  2010

• Venue

  Pattern Recognition Letters

• Publication date

  2010-10-01

• Fields of study

  Mathematics, Computer Science, Environmental Science

• Identifiers
  DOI 10.1016/J.PATREC.2010.03.014
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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