A decision tree algorithm based on attribute frequency splitting
Ren Xian-hua · Journal of Guangxi University of Technology · 2007
Decision tree is a usual method of classification in data mining.In the process of the decision tree constructing,the criteria of selecting partition attributes will influence the efficiency of classification.Based on the concept of attributes importance metric that is measured by the function of attribute frequency in Rough Set theory,and the metric being used to select the partition attribute,a new decision tree algorithm is proposed.In the algorithm,the function of attribute frequency is computed only using the discernibility matrix of data set.So,the computation is simple.The results of experiment show that compared with the entropy-based method,the decision tree constructed by the new algorithm is simpler in the structure,and the new algorithm can improve the efficiency of classification.