Abstract
As both rough sets theory and neural network in data mining have special advantages and exiting problems, this paper presented a combined algorithm based rough sets theory and BP neural network. This algorithm deducts data from data warehouse by using rough sets’ deduct function, and then moves the deducted data to the BP neural network as training data. By data deduct, the expression of training will become clearer, and the scale of neural network can be simplified. At the same time, neural network can easy up rough set’s sensitivity for noise data. This paper presents a cost function to express the relationship between the amount of training data and the precision of neural network, and to supply a standard for the change from rough set deduct to neural network training.
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Tao, K. (2010). A Novel Hybrid Data Mining Method Based on the RS and BP. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13318-3_43
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DOI: https://doi.org/10.1007/978-3-642-13318-3_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13317-6
Online ISBN: 978-3-642-13318-3
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