A Feature Selection Strategy for the Relevance Vector Machine

Armin Shmilovici Leib, David Ben-Shimon

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The Relevance Vector Machine (RVM) is a generalized linear model that can use kernel functions as basis functions. The typical RVM solution is very sparse. We present a strategy for feature ranking and selection via evaluating the influence of the features on the relevance vectors. This requires a single training of
the RVM, thus, it is very efficient. Experiments on a benchmark regression problem provide evidence that it selects high-quality feature sets at a fraction of the costs of classical methods.
Original languageAmerican English
Title of host publicationRECENT ADVANCES in KNOWLEDGE ENGINEERING and SYSTEMS SCIENCE
Subtitle of host publicationProceedings of the 12th International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases (AIKED '13)
EditorsZengshi Chen, Lopez-Neri Emmanuel
Pages73-78
StatePublished - 2013

Keywords

  • Feature Selection
  • Machine Learning
  • Relevance Vector Machine

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