DocumentCode :
1948525
Title :
Complementary Learning Fuzzy Neural Network: An Approach to Imbalanced Dataset
Author :
Tan, T.Z. ; Ng, G.S. ; Quek, C.
Author_Institution :
Nanyang Technol. Univ., Singapore
fYear :
2007
fDate :
12-17 Aug. 2007
Firstpage :
2306
Lastpage :
2311
Abstract :
Imbalanced dataset is a phenomenon seen in many real life applications, especially in medical field. The conventional computational intelligence algorithms cannot effectively handle the imbalanced data because they are designed for balanced data distribution. Complementary learning fuzzy neural network is proposed as one of the approach for learning imbalanced dataset. It is shown empirically and theoretically that the effects of imbalanced dataset are minimal in this class of neuro-fuzzy system.
Keywords :
data handling; fuzzy neural nets; learning (artificial intelligence); complementary learning fuzzy neural network; computational intelligence algorithms; imbalanced dataset; medical field; neuro-fuzzy system; Algorithm design and analysis; Computational intelligence; Cost function; Design methodology; Fuzzy neural networks; Linear discriminant analysis; Multilayer perceptrons; Neural networks; Support vector machine classification; Support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2007. IJCNN 2007. International Joint Conference on
Conference_Location :
Orlando, FL
ISSN :
1098-7576
Print_ISBN :
978-1-4244-1379-9
Electronic_ISBN :
1098-7576
Type :
conf
DOI :
10.1109/IJCNN.2007.4371318
Filename :
4371318
Link To Document :
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