基于lazy方法的数量型关联分类

传统关联分类方法处理数量型数据时，“先离散，再学习”的步骤使新的测试样例可能无法找到合适的离散区间，形成离散盲目性问题。基于lazy的数量型关联分类作为一种新的关联分类法，它首先利用K-近邻分类思想为测试样例求得K-近邻作为新的训练数据集，然后对包含测试样例和K个近邻的数据集离散化，并在K-近邻组成的离散数据集上挖掘关联规则并构造分类器进行分类。最后，通过与传统CBA、CMAR、CPAR算法在7个常用UCI数量型数据集上进行的对比实验结果表明，基于lazy的数量型关联分类方法的平均分类准确率提高了0.66%~1.65%，证明了该方法的可行性。

Quantitative associative classification based on lazy method

In order to avoid the problem of blind discretization of traditional classification "discretize first learn second", a new method of associative classification based on lazy thought was proposed. It discretized the new training dataset gotten by determining the K-nearest neighbors of test instance firstly, and then mined associative rules form the discrete dataset and built a classifier for predicting the class label of test instance. At last, the results of contrastive experiments with CBA (Classification Based on Associations), CMAR (Classification based on Multiple Class-Association Rules) and CPAR (Classification based on Predictive Association Rules) carried out on seven commonly used quantitative datasets of UCI show that the classification accuracy of the proposed method can be increased by 0.66% to 1.65%, and verify the feasibility of this method.