mproving the Input of Classified Neural Networks Through Feature Construction

SOFTWARE,ALGORITHM AND SIMULATION1. Pas~EClassification, as an important program of data mining, is to build a classified function or model that is able tomap the item Of database to one of given classes. The classification algorithm in common use mainly includebayesian, decision trees, mule induction and neural networks 1].The classification algorithm based on decision trees has been in maturity earlier in 1980s', typical of whichis ID3 by J. R. Quinlan 21, it introduced the …

Improving the Input of Classified Neural Networks Through Feature Construction

A general classification algorithm of neural networks is unable to obtain satisfied results because of the uncertain problems existing among the features in moot classification programs, such as interaction. With new features constructed by optimizing decision trees of examples, the input of neural networks is improved and an optimized classification algorithm based on neural networks is presented. A concept of dispersion of a classification program is also introduced too in this paper. At the end of the paper, an analysis is made with an example.