The classification of cancer based on DNA microarray data that uses diverse ensemble genetic programming.
OBJECTIVE
The classification of cancer based on gene expression data is one of the most important procedures in bioinformatics. In order to obtain highly accurate results, ensemble approaches have been applied when classifying DNA microarray data. Diversity is very important in these ensemble approaches, but it is difficult to apply conventional diversity measures when there are only a few training samples available. Key issues that need to be addressed under such circumstances are the development of a new ensemble approach that can enhance the successful classification of these datasets.


METHODS
An effective ensemble approach that does use diversity in genetic programming is proposed. This diversity is measured by comparing the structure of the classification rules instead of output-based diversity estimating.


RESULTS
Experiments performed on common gene expression datasets (such as lymphoma cancer dataset, lung cancer dataset and ovarian cancer dataset) demonstrate the performance of the proposed method in relation to the conventional approaches.


CONCLUSIONS
Diversity measured by comparing the structure of the classification rules obtained by genetic programming is useful to improve the performance of the ensemble classifier.
Hong
Jin-Hyuk
JH
Department of Computer Science, Yonsei University, 134 Sinchon-dong, Sudaemoon-ku, Seoul 120-749, Republic of Korea.
Cho
Sung-Bae
SB
D001185:Artificial Intelligence; D019295:Computational Biology; D020869:Gene Expression Profiling / Q000379:methods; D006801:Humans; D009369:Neoplasms / Q000145:classification*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*