Constructing Hybrid Selective Classifiers for Incomplete Data with Gain-Ratio
Chuanhuan Yin · Journal of Beijing Jiaotong University · 2009
Due to most selective classifiers mainly deal with complete data and actual data sets are often incomplete and have many redundant or irrelevant attributes,a hybrid selective classifier for incomplete data denoted as GBSD is presented in this paper.GBSD is based on former work and Information gain ratio.Experiment results on twelve standard incomplete data sets show that the GBSD not only can enormously reduce the number of attributes,but also can more effectively improve the accuracy and efficiency of classification than former work.