Abstract
Typical e:testors are useful to do feature selection in supervised classification problems with mixed incomplete data, where similarity function is not the total coincidence, but it is a one threshold function. In this kind of problems, modifications on the training matrix can appear very frequently. Any modification of the training matrix can change the set of all typical ε:testors, so this set must be recomputed after each modification. But, complexity of algorithms for calculating all typical ε:testors of a training matrix is too high. In this paper we analyze how the set of all typical ε:testors changes after modifications. An alternative method to calculate all typical ε:testors of the modified training matrix is exposed. The new method’s complexity is analyzed and some experimental results are shown.
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© 2004 Springer-Verlag Berlin Heidelberg
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Carrasco-Ochoa, J.A., Ruiz-Shulcloper, J., De-la-Vega-Doría, L.A. (2004). Feature Selection Using Typical ε: Testors, Working on Dynamical Data. In: Sanfeliu, A., Martínez Trinidad, J.F., Carrasco Ochoa, J.A. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2004. Lecture Notes in Computer Science, vol 3287. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30463-0_86
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DOI: https://doi.org/10.1007/978-3-540-30463-0_86
Publisher Name: Springer, Berlin, Heidelberg
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