Corpus ID: 15928472

Approximate False Positive Rate Control in Selection Frequency for Random Forest

  title={Approximate False Positive Rate Control in Selection Frequency for Random Forest},
  author={E. Konukoglu and M. Ganz},
  • E. Konukoglu, M. Ganz
  • Published 2014
  • Computer Science, Mathematics
  • ArXiv
  • Random Forest has become one of the most popular tools for feature selection. Its ability to deal with high-dimensional data makes this algorithm especially useful for studies in neuroimaging and bioinformatics. Despite its popularity and wide use, feature selection in Random Forest still lacks a crucial ingredient: false positive rate control. To date there is no efficient, principled and computationally light-weight solution to this shortcoming. As a result, researchers using Random Forest… CONTINUE READING
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