Prototype Classifier Design with Pruning

نویسندگان

  • Jiang Li
  • Michael T. Manry
  • Changhua Yu
  • D. Randall Wilson
چکیده

Algorithms reducing the storage requirement of the nearest neighbor classifier (NNC) can be divided into three main categories: Fast searching algorithms, Instance-based learning algorithms and Prototype based algorithms. In this paper an algorithm, called LVQPRU, is proposed for pruning NNC prototype vectors so that a compact classifier with good performance can be obtained. The basic condensing algorithm is applied to the initial prototypes to speed up the learning process. The learning vector quantization (LVQ) algorithm is utilized to fine tune the remaining prototypes during each pruning iteration. The LVQPRU algorithm is evaluated on several data sets along with 12 other algorithms using ten-fold cross-validation followed by a t-test. Simulation results show that the proposed algorithm has the highest generalization accuracies and good storage reduction ratios for most of the data sets.

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عنوان ژورنال:
  • International Journal on Artificial Intelligence Tools

دوره 14  شماره 

صفحات  -

تاریخ انتشار 2005