• DocumentCode
    1677999
  • Title

    A New Heterogeneous Dissimilarity Measure for Data Classification

  • Author

    Pereira, Cesar Lima ; Cavalcanti, George D C ; Ren, Tsang Ing

  • Author_Institution
    Center of Inf., Fed. Univ. of Pernambuco, Recife, Brazil
  • Volume
    2
  • fYear
    2010
  • Firstpage
    373
  • Lastpage
    374
  • Abstract
    Instance-based learning algorithms typically suffer influences of dissimilarity functions. The problem is frequently related to the Nearest Neighbor rules of these algorithms. This paper will introduce a new dissimilarity measure, called Heterogeneous Centered Difference Measure, which is tested over many known databases. The results are compared with other distance functions.
  • Keywords
    learning (artificial intelligence); pattern classification; data classification; heterogeneous centered difference measurement; heterogeneous dissimilarity measurement; instance-based learning algorithms; nearest neighbor rules; Accuracy; Databases; Equations; Mathematical model; Measurement; Nearest neighbor searches; Training; distance measure; nearest neighbor;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Tools with Artificial Intelligence (ICTAI), 2010 22nd IEEE International Conference on
  • Conference_Location
    Arras
  • ISSN
    1082-3409
  • Print_ISBN
    978-1-4244-8817-9
  • Type

    conf

  • DOI
    10.1109/ICTAI.2010.132
  • Filename
    5669990
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1677999