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Improving classification performance of Support Vector Machine by genetically optimising kernel shape and hyper-parameters

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Abstract

Support Vector Machines (SVMs) deliver state-of-the-art performance in real-world applications and are now established as one of the standard tools for machine learning and data mining. A key problem of these methods is how to choose an optimal kernel and how to optimise its parameters. The real-world applications have also emphasised the need to consider a combination of kernels—a multiple kernel—in order to boost the classification accuracy by adapting the kernel to the characteristics of heterogeneous data. This combination could be linear or non-linear, weighted or un-weighted. Several approaches have been already proposed to find a linear weighted kernel combination and to optimise its parameters together with the SVM parameters, but no approach has tried to optimise a non-linear weighted combination. Therefore, our goal is to automatically generate and adapt a kernel combination (linear or non-linear, weighted or un-weighted, according to the data) and to optimise both the kernel parameters and SVM parameters by evolutionary means in a unified framework. We will denote our combination as a kernel of kernels (KoK). Numerical experiments show that the SVM algorithm, involving the evolutionary kernel of kernels (eKoK) we propose, performs better than well-known classic kernels whose parameters were optimised and a state of the art convex linear and an evolutionary linear, respectively, kernel combinations. These results emphasise the fact that the SVM algorithm could require a non-linear weighted combination of kernels.

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Authors and Affiliations

  1. Laboratoire d’Informatique, de Traitement de l’Information et des Systèmes, EA 4108, Institut National des Sciences Appliquées, Rouen, France

    Laura Dioşan, Alexandrina Rogozan & Jean-Pierre Pecuchet

  2. Faculty of Mathematics and Computer Science, Babeş-Bolyai University, Cluj-Napoca, Romania

    Laura Dioşan

Authors
  1. Laura Dioşan
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  2. Alexandrina Rogozan
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  3. Jean-Pierre Pecuchet
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Correspondence to Laura Dioşan.

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Dioşan, L., Rogozan, A. & Pecuchet, JP. Improving classification performance of Support Vector Machine by genetically optimising kernel shape and hyper-parameters. Appl Intell 36, 280–294 (2012). https://doi.org/10.1007/s10489-010-0260-1

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