PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

The Feature Importance Ranking Measure
Alexander Zien, Nicole Krämer, Sören Sonnenburg and Gunnar Rätsch
In: ECML PKDD, September 7th to 11th, 2009, Bled, Slovenia.

Abstract

Most accurate predictions are typically obtained by learning machines with complex feature spaces (e.g., as induced by kernels). Unfortunately, such decision rules are hardly accessible to humans and cannot easily be used to gain insights about the application domain. Therefore, one often resorts to linear models in combination with variable selection, thereby sacrificing some predictive power for presumptive interpretability. Here, we introduce the Feature Importance Ranking Measure (FIRM), which by retrospective analysis of arbitrary learning machines allows to achieve both excellent predictive performance and superior interpretation. In contrast to standard raw feature weighting, FIRM takes the underlying correlation structure of the features into account. Thereby, it is able to discover the most relevant features, even if their appearance in the training data is entirely prevented by noise. The desirable properties of FIRM are investigated analytically and illustrated in a few simulations.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:5477
Deposited By:Alexander Zien
Deposited On:10 October 2009