Abstract
In this chapter we argue to use version spaces as an approach to reliable classification. 1 The key idea is to extend version spaces to contain the target hypothesis ht or hypotheses similar to ht. In this way, the unanimous-voting classification rule of version spaces is not capable of misclassifying new instances; i.e., instance classifications become reliable. We propose to implement version spaces using support vector machines. The resulting combination is called version space support vector machines (VSSVMs). Experiments show that VSSVMs are able to outperform the existing approaches to reliable classification.
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Smirnov, E., Nalbantov, G., Sprinkhuizen-Kuyper, I. (2012). Combining Version Spaces and Support Vector Machines for Reliable Classification. In: Dai, H., Liu, J., Smirnov, E. (eds) Reliable Knowledge Discovery. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-1903-7_6
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DOI: https://doi.org/10.1007/978-1-4614-1903-7_6
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