Abstract
Typical testors are useful for both feature selection and feature relevance determination in supervised classification problems. However, reported algorithms that address the problem of finding the set of all typical testors have exponential complexity. In this paper, we propose to adapt an evolutionary method, the Hill-Climbing algorithm, with an acceleration operator in mutation process, to address this problem in polinomial time. Experimental results with the method proposed are presented and compared, in efficiency, with other methods, namely, Genetic Algorithms (GA) and Univariate Marginal Distribution Algorithm (UMDA).
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Diaz-Sanchez, G. et al. (2011). Typical Testors Generation Based on an Evolutionary Algorithm. In: Yin, H., Wang, W., Rayward-Smith, V. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2011. IDEAL 2011. Lecture Notes in Computer Science, vol 6936. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23878-9_8
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DOI: https://doi.org/10.1007/978-3-642-23878-9_8
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