Review of the Extraction Methods of DNA Microarray Features Based on Central Decision Class Separation vs Rough Set Classifier

Piotr Artiemjew

doi:10.2478/v10209-011-0013-x

.blurhash-client-img { display: none !important; }

Review of the Extraction Methods of DNA Microarray Features Based on Central Decision Class Separation vs Rough Set Classifier

Foundations of Computing and Decision Sciences

Volume 37 (2012): Issue 4 (December 2012)

By: Piotr Artiemjew

Open Access

|Dec 2012

[1] Artiemjew P.: The Extraction Method of DNA Microarray Features Based on Experimental A Statistics. In: J.T. Yao et al. (eds.) RSKT 2011, Banff, Canada, LNCS, Springer, Heidelberg, vol. 6954, 2011, 642-648.
Open DOI Search in Google Scholar
[2] Artiemjew P.: The Extraction Method of DNA Microarray Features Based on Modified F Statistics vs Classifier Based on Rough Mereology. In: Kryszkiewicz, M., Rybinski, H., Skowron, A., Ras, Z., W. (eds.) ISMIS 2011, Warsaw, Poland, LNAI, Springer, Heidelberg, vol. 6804, 2011, 33-42.
Open DOI Search in Google Scholar
[3] Artiemjew P.: Classifiers based on rough mereology in analysis of DNA microarray data, in:, Sergy Pontoise France, IEEE Press, 2010.
Open DOI Search in Google Scholar
[4] 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.
Search in Google Scholar
[5] Artiemjew P.: Rough mereological classifiers obtained from weak rough set inclusions, in: Proceedings of Int. Conference on Rough Set and Knowledge Technology RSKT’08, Chengdu China, LNAI, Springer Verlag, Berlin, vol. 5009, 2008, 229-236.
Open DOI Search in Google Scholar
[6] Artiemjew P.: On classification of data by means of rough mereological granules of objects and rules, in: Proceedings of Int. Conference on Rough Set and Knowledge Technology RSKT’08, Chengdu China, LNAI, Springer Verlag, Berlin, vol. 5009, 2008, 221-228.
Open DOI Search in Google Scholar
[7] Artiemjew P.: Natural versus granular computing: Classifiers from granular structures, in: Proceedings of 6th International Conference on Rough Sets and Current Trends in Computing RSCTC’08, Akron, Ohio, USA, Springer Berlin / Heidelberg, vol. 5306, 2008, 150-159.
Open DOI Search in Google Scholar
[8] Artiemjew P.: Classifiers from granulated data sets: Concept dependent and layered granulation, in:, Warsaw Univ. Press, Warsaw, 2007, 1-9.
Search in Google Scholar
[9] Brown M., Grundy W., et al.: Knowledge-based analysis of microarray gene expression data by using support vector machines, University of California, 1999.
Search in Google Scholar
[10] Eisen MB, Brown PO: DNA arrays for analysis of gene expression. Methods Enzymol 303, 1999, 179-205.
Open DOI Search in Google Scholar
[11] Furey T.S., Cristianini, Duffy N., Bernarski, Schummer M., Haussler D.: ”Support Vector Machine Classification and Validation of Cancer Tissue Samples Using Microarray Expression Data,” Bioinformatics, vol. 16, 2000, 906-914.
Open DOI Search in Google Scholar
[12] Hájek P.: Metamathematics of Fuzzy Logic. Kluwer, Dordrecht, 1998.
Open DOI Search in Google Scholar
[13] Molinaro A.M., Simon R., Pfeiffer R.M.: Prediction error estimation: a comparison of resampling methods, in:, 2005, 3301-3307.
Open DOI Search in Google Scholar
[14] Polkowski L.: Toward rough set foundations. Mereological approach (a plenary lecture), in:, Uppsala, Sweden, 2004, LNAI, Springer Verlag, Berlin, vol. 3066, 2004, 8-25.
Open DOI Search in Google Scholar
[15] Polkowski L.: Formal granular calculi based on rough inclusions (a feature talk), in:, IEEE Press, 2005, 57-62.
Open DOI Search in Google Scholar
[16] Polkowski L.: Formal granular calculi based on rough inclusions (a feature talk), in:, IEEE Press, 2006, 57-62.
Open DOI Search in Google Scholar
[17] Polkowski L.: Granulation of knowledge in decision systems: The approach based on rough inclusions. The Method and its applications (plenary talk), in: Lecture Notes in Artificial Intelligence (Proceedings RSEiSP’07), Springer Verlag, Berlin, vol. 4585, 2005, 69-79.
Search in Google Scholar
[18] Polkowski L.: The paradigm of granular rough computing, in:, IEEE Computer Society, Los Alamitos, CA, 2007, 145-163.
Open DOI Search in Google Scholar
[19] Polkowski L.: A Unified Approach to Granulation of Knowledge and Granular Computing Based on Rough Mereology: A Survey, in: Handbook of Granular Computing, Witold Pedrycz, Andrzej Skowron, Vladik Kreinovich (Eds.), John Wiley & Sons, New York, 2008, 375-401.
Open DOI Search in Google Scholar
[20] Polkowski L., Artiemjew P.: On classifying mappings induced by granular structures. Transactions on Rough Sets IX. Lecture Notes in Computer Science, Springer Verlag, Berlin, vol. 5390, 2008, 264-286.
Search in Google Scholar
[21] Polkowski L., Artiemjew P.: A study in granular computing: On classifiers induced from granular reflections of data, Transactions on Rough Sets IX. Lecture Notes in Computer Science, Springer Verlag, Berlin, vol. 5390,2008, 230-263.
Search in Google Scholar
[22] Schena M.: Microarray analysis. Wiley, Hoboken, NJ, USA, 2003.
Search in Google Scholar
[23] http://tunedit.org/repo/RSCTC/2010/A
Search in Google Scholar
[24] Zadeh L.A.: Fuzzy sets and information granularity. In: Gupta, M., Ragade, R., Yager, R.R.(Eds.): Advances in Fuzzy Set Theory and Applications. North Holland, Amsterdam, 1979, 3-18.
Search in Google Scholar