The Extraction Method of DNA Microarray Features Based on Modified F Statistics vs. Classifier Based on Rough Mereology

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• 作者：Piotr Artiemjew (1) artem@matman.uwm.edu.pl
• 关键词：rough mereology &#8211 ; granular computing &#8211 ; rough sets &#8211 ; DNA microarrays &#8211 ; features extraction
• 刊名：Lecture Notes in Computer Science
• 出版年：2011
• 出版时间：2011
• 年：2011
• 卷：6804
• 期：1
• 页码：33-42
• 全文大小：193.5 KB
• 参考文献：1. Artiemjew, P.: Classifiers based on rough mereology in analysis of DNA microarray data. In: Proceedings 2010 IEEE International Conference on Soft Computing and Pattern Recognition SocPar 2010. IEEE Press, Sergy Pontoise France (2010)
  2. Artiemjew, P.: On strategies of knowledge granulation and applications to decision systems, PhD Dissertation, Polish Japanese institute of Information Technology, L. Polkowski, Supervisor, Warsaw (2009)
  3. Artiemjew, P.: On Classification of Data by Means of Rough Mereological Granules of Objects and Rules. In: Wang, G., Li, T., Grzymala-Busse, J.W., Miao, D., Skowron, A., Yao, Y. (eds.) RSKT 2008. LNCS (LNAI), vol. 5009, pp. 221–228. Springer, Heidelberg (2008)
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  7. Gorecki, P., Artiemjew, P.: DNA microarray classification by means of weighted voting based on rough set classifier. In: Proceedings 2010 IEEE International Conference on Soft Computing and Pattern Recognition SocPar 2010, pp. 269–272. IEEE Computer Society, Sergy Pontoise (2010)
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  12. Polkowski, L.: Formal granular calculi based on rough inclusions (a feature talk). In: Proceedings 2005 IEEE Int. Confrence on Granular Computing GrC 2005, pp. 57–62. IEEE Press, Los Alamitos (2005)
  13. Polkowski, L.: A Unified Approach to Granulation of Knowledge and Granular Computing Based on Rough Mereology: A Survey. In: Pedrycz, W., Skowron, A., Kreinovich, V. (eds.) Handbook of Granular Computing, pp. 375–401. John Wiley & Sons, New York (2008)
  14. Polkowski, L., Artiemjew, P.: On classifying mappings induced by granular structures. In: Peters, J.F., Skowron, A., Rybiński, H. (eds.) Transactions on Rough Sets IX. LNCS, vol. 5390, pp. 264–286. Springer, Heidelberg (2008)
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  16. http://tunedit.org/repo/RSCTC/2010/A
  17. Wojnarski, M., Janusz, A., Nguyen, H.S., Bazan, J., Luo, C., Chen, Z., Hu, F., Wang, G., Guan, L., Luo, H., Gao, J., Shen, Y., Nikulin, V., Huang, T.-H., McLachlan, G.J., Bošnjak, M., Gamberger, D.: RSCTC’2010 Discovery Challenge: Mining DNA Microarray Data for Medical Diagnosis and Treatment. In: Szczuka, M., Kryszkiewicz, M., Ramanna, S., Jensen, R., Hu, Q. (eds.) RSCTC 2010. LNCS, vol. 6086, pp. 4–19. Springer, Heidelberg (2010)
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• 作者单位：1. Department of Mathematics and Computer Science, University of Warmia and Mazury, Olsztyn, Poland
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

The paradigm of Granular Computing has emerged quite recently as an area of research on its own; in particular, it is pursued within the rough set theory initiated by Zdzisław Pawlak. Granules of knowledge can be used for the approximation of knowledge. Another natural application of granular structures is using them in the classification process. In this work we apply the granular classifier based on rough mereology, recently studied by Polkowski and Artiemjew 8_v1_w4 algorithm in exploration of DNA Microarrays. An indispensable element of the analysis of DNA microarray are the gene extraction methods, because of their high number of attributes and a relatively small number of objects, which in turn results in overfitting during the classification. In this paper we present one of our approaches to gene separation based on modified F statistics. The modification of F statistics, widely used in binary decision systems, consists in an extension to multiple decision classes and the application of a particular method to choose the best genes after their calculation for particular pairs of decision classes. The results of our research, obtained for modified F statistics, are comparable to, or even better than, the results obtained in other methods with data from the Advanced Track of the recent DNA Microarray data mining competition.