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FuSCa: A New Weighted Membership Driven Fuzzy Supervised Classifier

Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 27)

Abstract

The aim of this paper is to introduce a new supervised fuzzy classification methodology (FuSCa) to improve the performance of k-NN (k-Nearest Neighbor) algorithm based on the weighted nearest neighbor membership and global membership derived from the training dataset. In this classification method, the test object is assigned a class label having the maximum membership value for that corresponding class while a weighted membership vector is found after utilizing the Global and Nearest-Neighbor fuzzy membership vectors along with a global weight and a k-close weight respectively. FuSCa is compared with other approaches using the standard benchmark data-sets and found to produce better classification accuracy.

Keywords

Fuzzy membership Supervised classification Machine learning Data-mining Nearest neighbor Weighted membership 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Computer SciencePondicherry UniversityPondicherryIndia

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