一个多元逐步回归的二叉树分类器的设计与实现

The Design and Implementation of a Binary Tree Classifier Based on Multivariate Step-wise Regression

摘要: 作者设计并实现了一个基于多变元逐步回归的二叉树分类器．在树结构和特征子集的选择中采用了穷举法，比有限制条件的选择更合理更优化．用FORTRAN语言实现的“遍历”二叉树，充分利用了FORTRAN处理可调数组的能力，并采取适当技巧，从而最大限度地利用了计算机内存．该通用分类器，可用来对任何具有统计数据的模式进行分类．在对白血球的分类中，取得了五类97%，六类92.2%的高识别率．

Abstract: A binary tree classifier based on multivariate step-wise regression was designed and implemented.The "exhaustion" method was applied in selecting both the treestructure and the feature subsets to get more reasonable and optimal result than thatdone by selection with constrained condition.The "traversal" of binary tree was implemented with FORTRAN Language.Computer memory can be used sufficiently by both the advantage of FORTRAN for processing adjustable array and the programming skill.The classifier is universal and can be used in any pattern classification in whichthe statistical data is available.For its application to white blood cell usage the recognition rate is as high as 97% in five classes and 92.2% in six classes.

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