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23 June 1997 Equalizing the training set for neural network recognizer
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Abstract
The unequally training set causes the low classification rate of a neural network recognizer. In order to equalize the training set, two methods are proposed in this paper. The first way controls the training parameters according to the property of training samples, i.e. adjusts the study rate with a fuzzy rule. The fuzzy rule is defined by the distribution of the training set and the important level of each kind of samples. The classification rate can be improved in this way and the fast convergence property can be achieved. The second means of equalizing the training set reduces the over- represented samples by fuzzy clustering and increases the deficient samples by interpolating. The BP neural network is used as recognizer here. From the results of the computer simulations, the two methods show to be effective when the training data are imbalance. The two ways improve the classification rate of neural network recognizer by equalizing the training set.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yunhong Wang, Guo-Sui Liu, and Yiding Wang "Equalizing the training set for neural network recognizer", Proc. SPIE 3069, Automatic Target Recognition VII, (23 June 1997); https://doi.org/10.1117/12.277135
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