On the use of di erent classi cation rules in an editing task
نویسندگان
چکیده
Editing allows the selection of a representative subset of prototypes among the training sample to improve the performance of a classi cation task. The Wilson's editing algorithm was the rst proposal and then a great variety of new editing techniques have been proposed based on it. This algorithm consists on the elimination of prototypes in the training set that are misclassi ed using the k-NN rule. From such editing scheme, a general editing procedure can be straightforward derived, where any classi er beyond k-NN can be used. In this paper, we analyze the behavior of this general editing procedure combined with 3 di erent neighborhood-based classi cation rules, including k-NN. The results reveal better performances of the 2 other techniques with respect to k-NN in most of cases.
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