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A new fast prototype selection method based on clustering

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Abstract

In supervised classification, a training set T is given to a classifier for classifying new prototypes. In practice, not all information in T is useful for classifiers, therefore, it is convenient to discard irrelevant prototypes from T. This process is known as prototype selection, which is an important task for classifiers since through this process the time for classification or training could be reduced. In this work, we propose a new fast prototype selection method for large datasets, based on clustering, which selects border prototypes and some interior prototypes. Experimental results showing the performance of our method and comparing accuracy and runtimes against other prototype selection methods are reported.

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Notes

  1. These runtimes were obtained using an Intel Celeron CPU 2.4 GHz, 512 MB RAM.

  2. For SVM, we used the software from [28], for C4.5 and Naive Bayes WEKA [29] was used, LWLR and k-NN were implemented in MATLAB [30].

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Authors and Affiliations

  1. Computer Science Department, National Institute of Astrophysics, Optics and Electronics, Luis Enrrique Erro No. 1, Sta. María Tonantzintla, CP 72000, Puebla, Mexico

    J. Arturo Olvera-López, J. Ariel Carrasco-Ochoa & J. Francisco Martínez-Trinidad

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  1. J. Arturo Olvera-López
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  2. J. Ariel Carrasco-Ochoa
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  3. J. Francisco Martínez-Trinidad
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Correspondence to J. Arturo Olvera-López.

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Olvera-López, J.A., Carrasco-Ochoa, J.A. & Martínez-Trinidad, J.F. A new fast prototype selection method based on clustering. Pattern Anal Applic 13, 131–141 (2010). https://doi.org/10.1007/s10044-008-0142-x

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