微型机与应用Issue(23)：25-28,4.
逻辑模型树算法性能分析与改进研究
The research on analysis and improvement of logistic model tree
摘要
Abstract
Logistic Model Trees (LMT) algorithm is a classification algorithm which is based on tree induction and logistic regression. To verify the advantage of LMT, compare and analyze LMT with other decision tree methods on three UCI data sets. Because in logistic model trees, logistic regression models can lead to the high computational complexity. This issue can be addressed by using the AIC criterion to improve LMT. It can improve time performance of algorithm and prevent over fitting models. The modification of LMT is used on UCI data sets and tobacco comprehensive quality evaluation data. And the result demonstrates that this method is superior to other decision tree methods in model precision and recall rate and time performance is about 50%faster than the unimproved. It can evaluate tobacco comprehensive quality well.关键词
逻辑模型树/UCI标准数据集/烟叶综合质量评价数据/赤池信息量准则/模型精度/召回率Key words
logistic model tree/UCI data sets/tobacco comprehensive quality evaluation data/Akaike information criterion/model precision/recall rate分类
信息技术与安全科学引用本文复制引用
张艺梅,丁香乾,贺英,王丽丽,徐硕..逻辑模型树算法性能分析与改进研究[J].微型机与应用,2014,(23):25-28,4.基金项目
青岛市科技计划项目（12-4-1-9-JX）；国家科技支撑计划项目 （）
