Attribute-based reduction and its application in driving behavior
In order to solve the problem that common attribute reduction algorithms usually delete the attributes with practical significance,this paper proposes an improved attribute reduction algorithm.The algorithm mines the implicit relationship of the attribute with low weight,finds the attribute with the implicit relationship and analyzes and fuses it,and obtains the new attribute.While ensuring the reduction of attributes,the actual meaning of the attributes can be preserved.Experimental results show that the new attribute reduction algorithm improves the classification results on UCI public datasets by 4%and 7%,respectively,compared with the traditional random forest and SVM algorithms.Compared with the traditional random forest and SVM algorithms,the classification accuracy of the results obtained on the driving behavior dataset is improved by 6%and 8%,respectively.
万方数据
维普
