Opening the Black Box: Revealing Interpretable Sequence Motifs in Kernel-Based Learning Algorithms

Abstract. This work is in the context of kernel-based learning algorithms for sequence data. We present a probabilistic approach to automatically extract, from the output of such string-kernel-based learning algorithms, the subsequences—or motifs—truly underlying the machine’s predictions. The proposed framework views motifs as free parameters in a probabilistic model, which is solved through a global optimization approach. In contrast to prevalent approaches, the proposed method can discover even difficult, long motifs, and could be combined with any kernel-based learning algorithm that is based on an adequate sequence kernel. We show that, by using a discriminate kernel machine such as a support vector machine, the approach can reveal discriminative motifs underlying the kernel predictor. We demonstrate the efficacy of our approach through a series of experiments on synthetic and real data, including problems from handwritten digit recognition and a large-scale human splice sit...

• Type: Journal
• Year: 2015
• Where: PKDD
• Authors: Marina M.-C. Vidovic, Nico Görnitz, Klaus-Robert Müller, Gunnar Rätsch, Marius Kloft
• Keywords: Data Mining, PKDD 2015