Abstract: AUC assessment is the hot pot in data mining, in which B-AUC assessment can effectively assess the classifier performance, but the evaluation method has three disadvantages. This classification is based on two types of asymmetry, which impacts the evaluation results; by non-complete binary tree resulting in waste of storage space; the search based on partial binary tree is not efficient. This paper proposes BO-AUC assessment method which is based on complete binary tree structure and characteristics. This method can effectively solve the shortage of B-AUC. Further expansion of the standard, programmed algorithm is given. Experimental results show that the BO-AUC method achieves good results of classification.

Key words: Area Under roc Curve（AUC） evaluation, Binary-Area Under roc Curve（B-AUC）, complete binary tree, Optimized Binary-Area Under roc Curve（BO-AUC）, classifier performance

摘要： 分类技术是数据挖掘研究的核心技术之一，分类评估也是研究热点，基于AUC评估方法是分类评估领域的研究热点，其中B-AUC评估算法可以有效地评估分类器性能，但该评估方法有不足之处。该分类评估方法建立在不对称的两个类别上，影响了评价结果；根据非完全二叉树思想存储，浪费了存储空间；基于偏二叉树的搜索效率不高。利用完全二叉树的构造思想提出了BO-AUC评估方法，该方法将n个类别的分类问题分解为独立的二类进行成对的计算，可以有效地解决B-AUC的不足，进一步扩展基于AUC的评估标准，在MBNC实验上编程实现该方法，实验结果表明BO-AUC方法的有效性。

关键词: 曲线下的面积（AUC）评估, 基于二叉树方法求的曲线下的面积（B-AUC）, 完全二叉树, 优化的基于二叉树方法求的曲线下的面积（BO-AUC）, 分类器性能