DocumentCode
2770914
Title
Nominal-scale Evolving Connectionist Systems
Author
Watts, Michael J.
Author_Institution
Lincoln Univ., Canterbury
fYear
0
fDate
0-0 0
Firstpage
2055
Lastpage
2059
Abstract
A method is presented for extending the evolving connectionist system (ECoS) algorithm that allows it to explicitly represent and learn nominal-scale data without the need for an orthogonal or binary encoding scheme. Rigorous evaluation of the algorithm over benchmark data sets shows that it is able to learn, generalise and adapt well to classification problems. The algorithm is potentially useful for data mining tasks.
Keywords
learning (artificial intelligence); neural nets; binary encoding; data mining; evolving connectionist system algorithm; nominal-scale data; orthogonal encoding; Artificial neural networks; Computational intelligence; Data mining; Decision trees; Encoding; Fuzzy neural networks; Knowledge based systems; Neural networks; Neurons; Training data;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2006. IJCNN '06. International Joint Conference on
Conference_Location
Vancouver, BC
Print_ISBN
0-7803-9490-9
Type
conf
DOI
10.1109/IJCNN.2006.246974
Filename
1716364
Link To Document