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中文摘要：
鉴于已有的绝大多数选择性分类算法主要用于完整数据，而现实中的数据通常是不完整的并且包含许多冗余属性或无关属性，本文在已有工作基础上利用信息增益率构建了一种用于不完整数据的混合型的选择性贝叶斯分类器：GBSD．在12个标准的不完整数据集上的实验结果表明，GBSD不仅能大幅度减少属性数目，而且比已有工作更能有效改善分类准确率和效率．
英文摘要：
Due to most selective classifiers mainly deal with complete data and actual data sets are often incomplete and have many redundant or irrelevant attributes, a hybrid selective classifier for incomplete data denoted as GBSD is presented in this paper. GBSD is based on former work and Information gain ratio. Experiment results on twelve standard incomplete data sets show that the GBSD not only can enormously reduce the number of attributes, but also can more effectively improve the accuracy and efficiency of classification than former work.
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