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
Link To Document :
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