• DocumentCode
    3282030
  • Title

    Tree Decomposition of Multiclass Problems

  • Author

    Lorena, Ana C. ; de Carvalho, A.C.P.

  • Author_Institution
    Univ. Fed. do ABC, Santo Andre
  • fYear
    2008
  • fDate
    26-30 Oct. 2008
  • Firstpage
    189
  • Lastpage
    194
  • Abstract
    Several popular machine learning techniques are originally designed for the solution of two-class problems. However, several classification problems have more than two classes. One approach to deal with multiclass problems using binary classifiers is to decompose the multiclass problem into multiple binary subproblems disposed in a binary tree. This approach requires a binary partition of the classes for each node of the tree, which defines the tree structure. This paper presents two algorithms to determine the tree structure taking into account information collected from the used dataset. This approach allows the tree structure to be determined automatically for any multiclass dataset.
  • Keywords
    learning (artificial intelligence); pattern classification; tree data structures; binary classifier problem; binary tree structure decomposition; machine learning technique; multiclass dataset; Binary trees; Classification tree analysis; Clustering algorithms; Machine learning; Neural networks; Partitioning algorithms; Support vector machine classification; Support vector machines; Tree data structures; Voting; Machine Learning; decomposition strategies; multiclass classification;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2008. SBRN '08. 10th Brazilian Symposium on
  • Conference_Location
    Salvador
  • ISSN
    1522-4899
  • Print_ISBN
    978-1-4244-3219-6
  • Electronic_ISBN
    1522-4899
  • Type

    conf

  • DOI
    10.1109/SBRN.2008.43
  • Filename
    4665914