@InProceedings{ZieKraSonRae09,
	author = {Alexander Zien and Nicole Kr{\"a}mer and S{\"o}ren Sonnenburg and Gunnar R{\"a}tsch},
	title = {The Feature Importance Ranking Measure},
	year = {2009},
	booktitle = {In Proceedings of the European Conference on Machine Learning},
	editor = {W. Buntine and M. Grobelnik and D. Mladenic and J. Shawe-Taylor},
	volume = {5782/2009},
	pages = {694--709},
	publisher = {Springer Berlin / Heidelberg},
	edition = {XXIX},
	series = {Lecture Notes in Artificial Intelligence},
	pdf = {http://arxiv.org/pdf/0906.4258v1},
	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 presump- tive 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.}
}