Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Classification of Faces in Man and Machine
Neural Computation
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
Learning interpretable SVMs for biological sequence classification
RECOMB'05 Proceedings of the 9th Annual international conference on Research in Computational Molecular Biology
A general method for visualizing and explaining black-box regression models
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part II
An unsupervised approach to feature discretization and selection
Pattern Recognition
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Most accurate predictions are typically obtained by learning machines with complex feature spaces (as e.g. 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 simulations.