Title :
Robust neural learning from unbalanced data samples
Author :
Lu, Yi ; Guo, Hong ; Feldkamp, Lee
Author_Institution :
Dept. of Electr. & Comput. Eng., Michigan Univ., Dearborn, MI, USA
Abstract :
This paper describes the result of our study on neural learning to solve the classification problem in which the data is unbalanced and noisy. Our study was conducted on three different neural network architectures, multilayered back propagation, radial basis function, and fuzzy ARTMAP with training methods including duplicating minority class samples and the Snowball technique. Three major issues are addressed: neural learning from unbalanced data samples, neural learning from noise data, and making intentional biased decisions. The application considered in this study is classifying good(pass)/bad(fail) vehicles. Experiments are conducted on data samples downloaded directly from test sites of automobile assembly
Keywords :
ART neural nets; automobile industry; backpropagation; feedforward neural nets; fuzzy neural nets; multilayer perceptrons; noise; pattern classification; quality control; Snowball technique; automobile assembly; classification; fuzzy ARTMAP nets; minority class sample duplication; multilayered back propagation; neural network architectures; noise data; radial basis function nets; robust neural learning; unbalanced data samples; unbalanced noisy data; vehicles; Assembly; Automobiles; Automotive engineering; Data acquisition; Data engineering; Fuzzy neural networks; Industrial training; Neural networks; Robustness; Testing;
Conference_Titel :
Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
Conference_Location :
Anchorage, AK
Print_ISBN :
0-7803-4859-1
DOI :
10.1109/IJCNN.1998.687133