UNCERTAINTY AND A NEW MEASURE FOR CLASSIFICATION UNCERTAINTY


Damgacioglu H., İYİGÜN C.

10th International Conference on Fuzzy Logic and Intelligent Technologies in Nuclear Science (FLINS), İstanbul, Turkey, 26 - 29 August 2012, vol.7, pp.925-930 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 7
  • City: İstanbul
  • Country: Turkey
  • Page Numbers: pp.925-930

Abstract

Ben-Israel and Iyigun ([1] and [2]) presents a new clustering method which is probabilistic distance clustering (P-D Clustering). In this method, the probability of assignment to cluster for each point is inversely proportional to distances between data point and centers of clusters according to given number of clusters and their centers. In this paper, we study on new uncertainty measure for classification using the assignment probabilities of P-D Clustering. Moreover, the relationship of the new measure with Kullback - Liebner divergence is discussed.