Title :
Classification for Incomplete Data Using Classifier Ensembles
Author :
Jiang, Kai ; Chen, Haixia ; Yuan, Senmiao
Author_Institution :
CETC, Beijing
Abstract :
This paper proposes a neural network ensemble model for classification of incomplete data. In the method, the incomplete dataset is divided into a group of complete sub datasets, which is then used as the training sets for the neural networks. Compared with other methods dealing with missing data in classification, the proposed method can utilize all the information provided by the data with missing values, maintain maximum consistency of incomplete data and avoid the dependency on distribution assumption. Experiments on two UCI datasets show the superiority of the algorithm to other two typical treatments of missing data in ensemble learning
Keywords :
classification; data mining; database management systems; learning (artificial intelligence); neural nets; UCI datasets; ensemble learning; incomplete data classification; neural network ensemble model; Artificial neural networks; Computational efficiency; Computer science; Data mining; Educational institutions; Electronic mail; Neural networks; Probability; Statistical distributions; Statistics;
Conference_Titel :
Neural Networks and Brain, 2005. ICNN&B '05. International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7803-9422-4
DOI :
10.1109/ICNNB.2005.1614675