A selective neural network ensemble classification for incomplete data

A selective neural network ensemble classification for incomplete data Neural network ensemble (NNE) is a simple and effective method to deal with incomplete data for classification. However, with the increase in the number of missing values, the number of incomplete feature combinations (feature subsets) grown rapidly which makes the NNE method very time-consuming and the accuracy is also need to be improved. In this paper, we propose a selective neural network ensemble (SNNE) classification for incomplete data. The SNNE first obtains all the available feature subsets of the incomplete dataset and then applies mutual information to measure the importance (relevance) degree of each feature subset. After that, an optimization process is applied to remove the feature subsets by satisfying the following condition: there is at least a feature subset contained in the removed feature subset and the difference of their importance degree is smaller than a given threshold δ. Finally, the rest of the feature subsets were used to train a group of neural networks and the classification for a given sample is decided by weighted majority voting of all available components in the ensemble. Experimental results show that δ = 0.05 is reasonable in our study. It can improve the efficiency of the algorithm without loss the algorithm accuracy. Experiments also show that SNNE outperforms the NNE-based algorithms compared. In addition, it can greatly reduce the running time when dealing with datasets with larger number of missing values. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Machine Learning and Cybernetics Springer Journals

A selective neural network ensemble classification for incomplete data

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2016 by Springer-Verlag Berlin Heidelberg
Subject
Engineering; Computational Intelligence; Artificial Intelligence (incl. Robotics); Control, Robotics, Mechatronics; Complex Systems; Systems Biology; Pattern Recognition
ISSN
1868-8071
eISSN
1868-808X
D.O.I.
10.1007/s13042-016-0524-0
Publisher site
See Article on Publisher Site

Abstract

Neural network ensemble (NNE) is a simple and effective method to deal with incomplete data for classification. However, with the increase in the number of missing values, the number of incomplete feature combinations (feature subsets) grown rapidly which makes the NNE method very time-consuming and the accuracy is also need to be improved. In this paper, we propose a selective neural network ensemble (SNNE) classification for incomplete data. The SNNE first obtains all the available feature subsets of the incomplete dataset and then applies mutual information to measure the importance (relevance) degree of each feature subset. After that, an optimization process is applied to remove the feature subsets by satisfying the following condition: there is at least a feature subset contained in the removed feature subset and the difference of their importance degree is smaller than a given threshold δ. Finally, the rest of the feature subsets were used to train a group of neural networks and the classification for a given sample is decided by weighted majority voting of all available components in the ensemble. Experimental results show that δ = 0.05 is reasonable in our study. It can improve the efficiency of the algorithm without loss the algorithm accuracy. Experiments also show that SNNE outperforms the NNE-based algorithms compared. In addition, it can greatly reduce the running time when dealing with datasets with larger number of missing values.

Journal

International Journal of Machine Learning and CyberneticsSpringer Journals

Published: Apr 1, 2016

References

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