Improving the Input of Classified Neural Networks Through Feature Construction

Improving the Input of Classified Neural Networks

Through Feature Construction

佚名

【期刊名称】《系统工程与电子技术（英文版）》 【年(卷),期】2001(012)003

【摘要】A general classification algorithm of neural networks is unable to obtain satisfied results because of the uncertain problems existing among the features in most 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.`` 【总页数】5页(85-89)

【关键词】Feature construction;Neural networks;Dispersion;Decision trees;Hyperplane 【作者】佚名

【作者单位】Shool of Economics & Management of Tongji University, Shanghai, 200092, P. R. China;Research Institute of Software Technology of Qingdao University, 266071, P. R. China;Shool of Economics & Management of Tongji University, Shanghai, 200092, P. R. China 【正文语种】中文

【中图分类】TP3 【相关文献】

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