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Prototypes Reduction and Feature Selection based on Fuzzy Boundary Area for Nearest Neighbor Classifiers

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Abstract

For prototype-based classifiers, the number of prototypes results in increasing the computational time so that it takes very long time for a prototype-based classifier to determine the class label of an associated data. Many researchers have been interested in the reduction of the number of prototypes without degradation of the classification ability of prototype-based classifiers. In this paper, we introduce a new method for generating prototypes based on the assumption that the prototypes positioned near the boundary surface are important for improving the classification abilities of nearest neighbor classifiers. The main issue of this paper is how to locate the new prototypes as close as possible to the boundary surface. To realize this, we consider possibilistic C-Means clustering and conditional C-Means clustering. The clusters obtained by using possibilistic C-Means clustering methods are used to define the boundary areas, and the conditional fuzzy C-Means clustering technique is used to determine the locations of prototypes within the already defined boundary areas. The design procedure is illustrated with the aid of numeric examples that provide a thorough insight into the effectiveness of the proposed method.

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Acknowledgement

This paper was supported by Wonkwang University in 2017.

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Authors and Affiliations

  1. Department of Electronics Convergence Engineering, Wonkwang University, Iksan, Chon-Buk, 570-749, Korea

    Tae-Chon Ahn & Jihong Wang

  2. Department of Electrical Engineering, University of Suwon, Hwasung, 183-23, Korea

    Seok-Beom Roh

  3. Department of Computer Engineering, Daejeon University, Daejeon, 300-716, South Korea

    Yong Soo Kim

  4. Electronics and Information Engineering, Hengshui University, Hengshui, Hebei, China

    Jihong Wang

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  1. Tae-Chon Ahn
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Correspondence to Tae-Chon Ahn.

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Ahn, TC., Roh, SB., Kim, Y.S. et al. Prototypes Reduction and Feature Selection based on Fuzzy Boundary Area for Nearest Neighbor Classifiers. Int. J. Fuzzy Syst. 21, 639–654 (2019). https://doi.org/10.1007/s40815-018-0562-8

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  • DOI: https://doi.org/10.1007/s40815-018-0562-8

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