Neural network ensemble with negatively correlated features for cancer classification

Authors:
Hong-Hee Won;Sung-Bae Cho
Affiliations:
Dept. of Computer Science, Yonsei University, Seoul, Korea;Dept. of Computer Science, Yonsei University, Seoul, Korea
Venue:
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Year:
2003

Citing 0
Cited 2

Indexing ICD-9 codes for free-textual clinical diagnosis records by a new ensemble classifier

International Journal of Computational Intelligence in Bioinformatics and Systems Biology
Tumor classification by combining PNN classifier ensemble with neighborhood rough set based gene reduction

Computers in Biology and Medicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

The development of microarray technology has supplied a large volume of data to many fields. In particular, it has been applied to prediction and diagnosis of cancer, so that it expectedly helps us to exactly predict and diagnose cancer. It is essential to efficiently analyze DNA microarray data because the amount of DNA microarray data is usually very large. Since accurate classification of cancer is very important issue for treatment of cancer, it is desirable to make a decision by combining the results of various expert classifiers rather than by depending on the result of only one classifier. In spite of many advantages of ensemble classifiers, ensemble with mutually error-correlated classifiers has a limit in the performance. In this paper, we propose the ensemble of neural network classifiers learned from negatively correlated features to classify cancer precisely, and systematically evaluate the performance of the proposed method using three benchmark datasets. Experimental results show that the neural network ensemble with negatively correlated features produces the best recognition rate on the three benchmark datasets.