Title :
A new method for filling missing values by gray relational analysis
Author :
Han, Bingwei ; Xiao, Shuangjiu ; Liu, Lu ; Wu, Zhijing
Author_Institution :
Digital Art Lab., Shanghai Jiao Tong Univ., Shanghai, China
Abstract :
In Data Mining and Machine Learning, the missing attribute will have a negative impact on the learning results. The filling of missing values is a very challenging work. In this paper, a new algorithm based on gray relational analysis is presented, which takes the differences of the relationships between the properties into account. When calculating the gray relational grade, the weights of attributes will be considered. The experimental results demonstrate that this method performs well when filling the discrete missing values.
Keywords :
data mining; learning (artificial intelligence); attributes weight; data mining; filling missing values; grey relational analysis; machine learning; missing attribute; relational grade; Algorithm design and analysis; Data mining; Decision trees; Filling; Machine learning; Mutual information; Rain; gray relational analysis; missing values; mutual information;
Conference_Titel :
Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC), 2011 2nd International Conference on
Conference_Location :
Deng Leng
Print_ISBN :
978-1-4577-0535-9
DOI :
10.1109/AIMSEC.2011.6010428