• DocumentCode
    456459
  • Title

    Data Mining using Pruned Artificial Neural Network Tree (ANNT)

  • Author

    Anbananthen, Kalaiarasi S. ; Sainarayanan, G. ; Chekima, Ali ; Teo, Jason

  • Author_Institution
    Sch. of Eng. & Inf. Technol., Univ. Malaysia Sabah
  • Volume
    1
  • fYear
    0
  • fDate
    0-0 0
  • Firstpage
    1350
  • Lastpage
    1356
  • Abstract
    Artificial neural network (ANN) has not been effectively utilized in data mining because of the "black box" nature. This issue was resolved by using artificial neural network tree (ANNT) approach in our earlier works. Future improvement was made by incorporating pruning in ANNT approach. ANNT pruning approach consists of three phases: training, pruning and rule extraction. The training phase is concerned with ANN learning followed by pruning. In pruning, the redundant links from the trained network are deleted, rules are extracted from the pruned network. The proposed scheme results in extracting rules from contributing links and indirectly reduces the number of rules but maintaining classification accuracy
  • Keywords
    data mining; learning (artificial intelligence); neural nets; trees (mathematics); ANN learning; ANNT pruning; artificial neural network tree; data mining; rule extraction; training phase; Algorithm design and analysis; Artificial intelligence; Artificial neural networks; Data mining; Decision trees; Information technology; Machine learning; Neural networks; Pattern recognition; Statistics;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information and Communication Technologies, 2006. ICTTA '06. 2nd
  • Conference_Location
    Damascus
  • Print_ISBN
    0-7803-9521-2
  • Type

    conf

  • DOI
    10.1109/ICTTA.2006.1684577
  • Filename
    1684577