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Classification of Categorical Data in the Feature Space of Monotone DNFs

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 10614)


Nowadays, kernel based classifiers, such as SVM, are widely used on many different classification tasks. One of the drawbacks of these kind of approaches is their poor interpretability. In the past, some efforts have been devoted in designing kernels able to construct a more understandable feature space, e.g., boolean kernels, but only combinations of simple conjunctive clauses have been proposed.

In this paper, we present a family of boolean kernels, specifically, the Conjunctive kernel, the Disjunctive kernel and the DNF-kernel. These kernels are able to construct feature spaces with a wide spectrum of logical formulae. For all of these kernels, we provide a description of their corresponding feature spaces and efficient ways to calculate their values implicitly. Experiments on several categorical datasets show the effectiveness of the proposed kernels.


  • Kernel methods
  • Boolean kernels
  • DNF
  • SVM

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  • DOI: 10.1007/978-3-319-68612-7_32
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Correspondence to Mirko Polato .

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Polato, M., Lauriola, I., Aiolli, F. (2017). Classification of Categorical Data in the Feature Space of Monotone DNFs. In: Lintas, A., Rovetta, S., Verschure, P., Villa, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2017. ICANN 2017. Lecture Notes in Computer Science(), vol 10614. Springer, Cham.

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