Kim Jung Won, Choi Jong Uk, Choi Hong Yun, Chung Yoon

The Transactions of the Korea Information Processing Society (1994 ~ 2000), Vol. 1, No. 2, pp. 225-236, Jul. 1994
10.3745/KIPSTE.1994.1.2.225,   PDF Download:

Abstract

Many research results conducted by neural network researchers claimed that the degree of generalization of the neural network system is higher or at least equal to that of statistical methods. However, those successful results could be brought only if the neural network was trained by appropriately sound data, having a little of noisy data and being large enough to control noisy data. Real data used in a lot of fields, especially business fields, were not so sound that the network gave frequently failed to obtain satisfactory prediction accuracy, the degree of generalization. Enhancing the degree of generalization with noisy data is discussed in this study. The suggestion. which was obtained through a series of experiment, to enhance the degree of generalization is to remove inconsistent data by checking overlapping and inconsistencies. Furthermore, the previous conclusion by other reports is also confirmed that the learning mechanism of neural network takes average value of two inconsistent data included in training set[2]. The interim results of on-going research project are reported is this paper. These are an architecture of the neural network adopted in this project and the whole idea of developing on-line credit evaluation system, being integration of the expert(reasoning) system and the neural network(learning)system. Another definite result is corroborated through this study that quickprop, being adopted as a learning algorithm, also has more speedy learning process than does back propagation even in very noisy environment.

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