Table of Contents Author Guidelines Submit a Manuscript
Computational and Mathematical Methods in Medicine
Volume 2015 (2015), Article ID 193406, 11 pages
http://dx.doi.org/10.1155/2015/193406
Research Article

Genetic Programming Based Ensemble System for Microarray Data Classification

1Software School of Xiamen University, Xiamen, Fujian 361005, China
2Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon 999077, Hong Kong
3Baidu Inc., Beijing 100000, China
4School of Computer Engineering, Jimei University, Xiamen, Fujian 361021, China

Received 22 October 2014; Revised 1 January 2015; Accepted 19 January 2015

Academic Editor: John Mitchell

Copyright © 2015 Kun-Hong Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. S. Geman, E. Bienenstock, and R. Doursat, “Neural networks and the bias/variance dilemma,” Neural Computation, vol. 4, no. 1, pp. 1–58, 1992. View at Publisher · View at Google Scholar
  2. A. Krogh and J. Vedelsby, “Neural network ensembles, cross validation, and active learning,” in Advances in Neural Information Processing Systems, pp. 231–238, MIT Press, 1995. View at Google Scholar
  3. L. I. Kuncheva and C. J. Whitaker, “Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy,” Machine Learning, vol. 51, no. 2, pp. 181–207, 2003. View at Publisher · View at Google Scholar · View at Scopus
  4. L. Breiman, “Bagging predictors,” Machine Learning, vol. 24, no. 2, pp. 123–140, 1996. View at Google Scholar · View at Scopus
  5. Y. Freund and R. Schapire, “A desicion-theoretic generalization of on-line learning and an application to boosting,” in Computational Learning Theory, P. Vitányi, Ed., vol. 904 of Lecture Notes in Computer Science, pp. 23–37, Springer, Berlin, Germany, 1995. View at Publisher · View at Google Scholar
  6. T. K. Ho, “Random decision forests,” in Proceedings of the 3rd International Conference on Document Analysis and Recognition, vol. 1, pp. 278–282, Montreal, Canada, August 1995.
  7. S. D. Bay, “Combining nearest neighbor classifiers through multiple feature subsets,” in Proceedings of the 15th International Conference on Machine Learning, pp. 37–45, 1998.
  8. T. K. Ho, “The random subspace method for constructing decision forests,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 8, pp. 832–844, 1998. View at Publisher · View at Google Scholar · View at Scopus
  9. R. Bryll, R. Gutierrez-Osuna, and F. Quek, “Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets,” Pattern Recognition, vol. 36, no. 6, pp. 1291–1302, 2003. View at Publisher · View at Google Scholar · View at Scopus
  10. L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001. View at Publisher · View at Google Scholar · View at Scopus
  11. J. J. Rodríguez, L. I. Kuncheva, and C. J. Alonso, “Rotation forest: a new classifier ensemble method,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 10, pp. 1619–1630, 2006. View at Publisher · View at Google Scholar · View at Scopus
  12. R. Maclin and J. W. Shavlik, “Combining the predictions of multiple classifiers: using competitive learning to initialize neural networks,” in Proceedings of the 14th International Joint Conference on Artificial Intelligence, pp. 524–531, Montreal, Canada, 1995.
  13. K.-J. Kim and S.-B. Cho, “An evolutionary algorithm approach to optimal ensemble classifiers for DNA microarray data analysis,” IEEE Transactions on Evolutionary Computation, vol. 12, no. 3, pp. 377–388, 2008. View at Publisher · View at Google Scholar · View at Scopus
  14. D. Ruta and B. Gabrys, “Classifier selection for majority voting,” Information Fusion, vol. 6, no. 1, pp. 63–81, 2005. View at Publisher · View at Google Scholar · View at Scopus
  15. W. B. Langdon and B. F. Buxton, “Genetic programming for mining DNA chip data from cancer patients,” Genetic Programming and Evolvable Machines, vol. 5, no. 3, pp. 251–257, 2004. View at Publisher · View at Google Scholar · View at Scopus
  16. J. J. Yu, J. D. Yu, A. A. Almal et al., “Feature selection and molecular classification of cancer using genetic programming,” Neoplasia, vol. 9, no. 4, pp. 292–303, 2007. View at Publisher · View at Google Scholar · View at Scopus
  17. J.-H. Hong and S.-B. Cho, “The classification of cancer based on DNA microarray data that uses diverse ensemble genetic programming,” Artificial Intelligence in Medicine, vol. 36, no. 1, pp. 43–58, 2006. View at Publisher · View at Google Scholar · View at Scopus
  18. K.-H. Liu and C.-G. Xu, “A genetic programming-based approach to the classification of multiclass microarray datasets,” Bioinformatics, vol. 25, no. 3, pp. 331–337, 2009. View at Publisher · View at Google Scholar · View at Scopus
  19. L. I. Kuncheva, “A theoretical study on six classifier fusion strategies,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 2, pp. 281–286, 2002. View at Publisher · View at Google Scholar · View at Scopus
  20. L. Breiman, “Heuristics of instability and stabilization in model selection,” The Annals of Statistics, vol. 24, no. 6, pp. 2350–2383, 1996. View at Publisher · View at Google Scholar · View at MathSciNet
  21. K. Liu, M. Tong, S. Xie, and Z. Zeng, “Fusing decision trees based on genetic programming for classification of microarray datasets,” in Intelligent Computing Methodologies: 10th International Conference, ICIC 2014, Taiyuan, China, August 3–6, 2014. Proceedings, vol. 8589 of Lecture Notes in Computer Science, pp. 126–134, Springer International Publishing, Cham, Switzerland, 2014. View at Publisher · View at Google Scholar
  22. I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, “Gene selection for cancer classification using support vector machines,” Machine Learning, vol. 46, no. 1–3, pp. 389–422, 2002. View at Publisher · View at Google Scholar · View at Scopus
  23. V. Bolón-Canedo, N. Sánchez-Maroño, and A. Alonso-Betanzos, “An ensemble of filters and classifiers for microarray data classification,” Pattern Recognition, vol. 45, no. 1, pp. 531–539, 2012. View at Publisher · View at Google Scholar · View at Scopus
  24. F. Pedregosa, G. Varoquaux, A. Gramfort et al., “Scikit-learn: machine learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011. View at Google Scholar · View at MathSciNet
  25. Y. Sun, “Iterative RELIEF for feature weighting: algorithms, theories, and applications,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 6, pp. 1035–1051, 2007. View at Publisher · View at Google Scholar · View at Scopus
  26. Y. Sun, S. Goodison, J. Li, L. Liu, and W. Farmerie, “Improved breast cancer prognosis through the combination of clinical and genetic markers,” Bioinformatics, vol. 23, no. 1, pp. 30–37, 2007. View at Publisher · View at Google Scholar · View at Scopus
  27. D. Albanese, R. Visintainer, S. Merler, S. Riccadonna, G. Jurman, and C. Furlanello, “mlpy: machine learning python,” Tech. Rep., 2012. View at Google Scholar
  28. C. S. Perone, “Pyevolve: a python open-source framework for genetic algorithms,” ACM SIGEVOlution, vol. 4, no. 1, pp. 12–20, 2009. View at Publisher · View at Google Scholar
  29. M. C. X. Tong, K.-H. Liu, C. G. Xu, and W. B. Ju, “An ensemble of SVM classifiers based on gene pairs,” Computers in Biology and Medicine, vol. 43, no. 6, pp. 729–737, 2013. View at Publisher · View at Google Scholar · View at Scopus
  30. M. Sokolova and G. Lapalme, “A systematic analysis of performance measures for classification tasks,” Information Processing and Management, vol. 45, no. 4, pp. 427–437, 2009. View at Publisher · View at Google Scholar · View at Scopus
  31. B. Ayerdi and M. Graña, “Hybrid extreme rotation forest,” Neural Networks, vol. 52, pp. 33–42, 2014. View at Publisher · View at Google Scholar · View at Scopus
  32. https://github.com/borjaayerdi/AdaHERF.
  33. E. F. Petricoin III, A. M. Ardekani, B. A. Hitt et al., “Use of proteomic patterns in serum to identify ovarian cancer,” The Lancet, vol. 359, no. 9306, pp. 572–577, 2002. View at Publisher · View at Google Scholar · View at Scopus
  34. T. R. Golub, D. K. Slonim, P. Tamayo et al., “Molecular classification of cancer: class discovery and class prediction by gene expression monitoring,” Science, vol. 286, no. 5439, pp. 531–527, 1999. View at Publisher · View at Google Scholar · View at Scopus
  35. U. Alon, N. Barka, D. A. Notterman et al., “Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays,” Proceedings of the National Academy of Sciences of the United States of America, vol. 96, no. 12, pp. 6745–6750, 1999. View at Publisher · View at Google Scholar · View at Scopus
  36. G. J. Gordon, R. V. Jensen, L.-L. Hsiao et al., “Translation of microarray data into clinically relevant cancer diagnostic tests using gene expression ratios in lung cancer and mesothelioma,” Cancer Research, vol. 62, no. 17, pp. 4963–4967, 2002. View at Google Scholar · View at Scopus
  37. D. Singh, P. G. Febbo, K. Ross et al., “Gene expression correlates of clinical prostate cancer behavior,” Cancer Cell, vol. 1, no. 2, pp. 203–209, 2002. View at Publisher · View at Google Scholar · View at Scopus
  38. D. G. Beer, S. L. R. Kardia, C.-C. Huang et al., “Gene-expression profiles predict survival of patients with lung adenocarcinoma,” Nature Medicine, vol. 8, no. 8, pp. 816–824, 2002. View at Publisher · View at Google Scholar · View at Scopus
  39. J. Khan, J. S. Wei, M. Ringnér et al., “Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks,” Nature Medicine, vol. 7, no. 6, pp. 673–679, 2001. View at Publisher · View at Google Scholar · View at Scopus
  40. S. A. Armstrong, J. E. Staunton, L. B. Silverman et al., “MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia,” Nature Genetics, vol. 30, no. 1, pp. 41–47, 2002. View at Publisher · View at Google Scholar · View at Scopus
  41. C. M. Perou, T. Sørile, M. B. Eisen et al., “Molecular portraits of human breast tumours,” Nature, vol. 406, no. 6797, pp. 747–752, 2000. View at Publisher · View at Google Scholar · View at Scopus