期刊名称：International Journal of Soft Computing & Engineering
电子版ISSN：2231-2307
出版年度：2011
卷号：1
期号：3
页码：98-101
出版社：International Journal of Soft Computing & Engineering
摘要：Decision trees create an easily understood structure for evaluating complex decisions. Tree Boost models often have a degree of accuracy that cannot be obtained using a large, single-tree model. Tree Boost models are adaptable, easy to interpret and often equal to or superior to any other predictive functions including neural networks. In this paper, the performance of the proposed approach based on Stochastic Gradient Boosted Decision Trees based method is demonstrated on the DAMADICS benchmark problem. An attempt has been made to improve the performance of fault diagnosis task on DAMADICS benchmark.
