分类规则挖掘的免疫算法

为了高效地从数据库中挖掘分类规则，提出了一种基于免疫算法的分类算法．该算法的核心思想为：对规则的前件进行固定长度编码，适应度函数的计算由分类规则的较小分类错误率、简洁性、一致性和训练实例的覆盖性构成，通过把适应度最小的个体作为先验知识来修改个体的某些分量的方法进行疫苗接种，并通过检测个体是否出现退化和模拟退火来实现免疫选择，同时还采用了基于信息增益的规则剪枝策略．在美国加州大学标准数据集中的5个数据集上将该算法与RISE和OCEC算法进行了实验比较，结果表明该算法不仅具有更快的收敛速度，而且获得了更高的预测准确率及更小的规则集。

Mining of Classification Rule Based on Immune Algorithm

To efficiently mine the classification rule from databases, a novel classification algorithm based on immune algorithm was proposed. The core of the immune classification algorithm is as follows. The rule antecedent is encoded as fixed-length chromosome; The fitness function is calculated according to minor misclassification ratio, simplicity and consistency of rules, and coverage ratio of training examples; A vaccination is accomplished by modifying genes on some bits in accordance with minimal fitness function which serves as prior knowledge; Immune selection is accomplished by testing whether a serious degeneration has happened in the evolutionary process and annealing selection. Meanwhile, a rule pruning procedure based on information gain was designed for improving the comprehensibility of classification rule mined. The algorithm has been compared with RISE and OCEC algorithms with five benchmark datasets from UCI data set repository. Experimental results show that the proposed algorithm not only has faster convergence speed, but also can achieve higher prediction accuracy with less number of rules.