Typical ε: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. © Springer-Verlag 2004.
CITATION STYLE
Carrasco-Ochoa, J. A., Ruiz-Shulcloper, J., & De-la-Vega-Doría, L. A. (2004). Feature selection using typical ε:testors, working on dynamical data. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3287, 685–692. https://doi.org/10.1007/978-3-540-30463-0_86
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