为提高名词性属性实例差异的识别精度,优化分类算法性能,综合考虑实例的属性和类别特征,提出了一种基于条件概率分布的混合距离度量方法.首先,计算属性间以及属性与类别间条件概率分布的差异;其次,利用互信息对2种差异进行加权组合,得到新的混合距离度量;最后,利用K-近邻算法在20个UCI(University of California Irvine)数据集上进行仿真实验,并将其应用于儿童阑尾炎的诊断和治疗.结果表明:较重叠度量等3种度量方法,本文提出的距离度量方法,显著提高了分类算法的准确率,具有较好的应用前景.
To improve the recognition accuracy of nominal attribute instance differences and optimize the performance of classification algorithms, a hybrid distance measurement method based on conditional probability distribution is proposed, which comprehensively considers the attributes and category features of instances.Firstly, this method calculates the differences in conditional probability distributions among attributes and between attributes and categories. Secondly, a new hybrid distance metric is obtained by using mutual information to weight and combine the two differences. Finally, simulation experiments are performed on 20 UCI datasets based on K-nearest neighbor algorithm. Meanwhile, the distance measurement method is applied to the diagnosis and treatment of appendicitis in children. The results show that compared with the three measurement methods including overlap measurement, the distance measurement method proposed in this work significantly improves the accuracy of the classification algorithm and has good application prospects.
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