Decompositional Rule Extraction from Support Vector Machines by Active Learning

@article{Martens2009DecompositionalRE,
  title={Decompositional Rule Extraction from Support Vector Machines by Active Learning},
  author={David Martens and Bart Baesens and Tony Van Gestel},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2009},
  volume={21},
  pages={178-191}
}
Support vector machines (SVMs) are currently state-of-the-art for the classification task and, generally speaking, exhibit good predictive performance due to their ability to model nonlinearities. However, their strength is also their main weakness, as the generated nonlinear models are typically regarded as incomprehensible black-box models. In this paper, we propose a new active learning-based approach (ALBA) to extract comprehensible rules from opaque SVM models. Through rule extraction… CONTINUE READING

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