一种高效的分类规则挖掘算法

High Efficient Algorithm for Classification Rule

提出了一种高效的分类规则挖掘算法,它结合神经网络的容错性能和决策树的规则生成能力,采用神经网络从样本集中删除不相关的和弱相关的特征属性,同时删除训练样本集中的噪声数据。然后采用决策树从处理过的训练样本集中抽取规则,由于去除了噪声数据,因此使得所挖掘的规则精确度大大提高,同时减少了规则的数目。实验证明所提出的算法,具有很高的分类精度。

This paper presents a new high efficient classification algorithm,It integrats the Neural Network and decision tree.The Neural Network is used to reduce the irrelevance feature set and filter the noise data in the training dataset, The decision tree extracts rule set from the worked training dataset.h can enhance the precision of classification,generalization performance and reduce the number of rule.The experiment demonstrates the effectiveness of the mentioned algorithm.