A selective Bayes classifier with meta-heuristics for incomplete data

Neurocomputing

Volumn 106, Issue , 2013, Pages 95-102

  Lin, Hung Chun ^a   Su, Chao Ton ^a  

^a NATIONAL TSING HUA UNIVERSITY   (Taiwan)

Author keywords

Electromagnetism like Mechanism; Feature selection; Incomplete data; Missing at random; Robust Bayes Classifier

Indexed keywords

BAYES CLASSIFIER; CLASSIFICATION APPROACH; CLASSIFICATION PERFORMANCE; ELECTROMAGNETISM-LIKE MECHANISM ALGORITHMS; ELECTROMAGNETISM-LIKE MECHANISMS; FEATURE SELECTION AND CLASSIFICATION; INCOMPLETE DATA; MISSING AT RANDOMS;

ALGORITHMS; COMPUTATIONAL COMPLEXITY; ELECTROMAGNETISM; FEATURE EXTRACTION;

CLASSIFICATION (OF INFORMATION);

ARTICLE; CLASSIFICATION ALGORITHM; CLASSIFIER; CONTROLLED STUDY; DATA PROCESSING; ELECTROMAGNETISM LIKE MECHANISM ALGORITHM; FEATURE SELECTION; GENETIC ALGORITHM; INCOMPLETE DATA; INFORMATION PROCESSING; INTERMETHOD COMPARISON; LEARNING ALGORITHM; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; MEASUREMENT ACCURACY; PRIORITY JOURNAL; PROCESS DEVELOPMENT; ROBUST BAYES CLASSIFIER;

EID: 84875402925     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2012.10.020     Document Type: Article

References (37)

1. Quinlan J.R. C4.5: Programs for Machine Learning 1993, Morgan Kaufman, San Francisco.
2. P. Langley, W. Iba, K. Thompson, An analysis of Bayesian classifiers, in: Proceedings of the Tenth National Conference on Artificial Intelligence, San Jose, California, July 12-16 1992, pp. 223-228.
3. Duda R.O., Hart P.E. Pattern Classification and Scene Analysis 1973, Wiley, New York.
4. Kohavi R., Becker B., Sommerfield D. Improving simple Bayes. Poster Papers of the ECML-97 1997, Charles University, Prague, pp. 78-87. M. van Someren, G. Widmer (Eds.).
5. Dempster A.P., Laird N.M., Rubin D.B. Maximum likelihood from incomplete data via the EM algorithm. J. R. Statist. Soc. Ser. B 1977, 39(1):1-38.
6. Stanimirova I., Walczak B. Classification of data with missing elements and outliers. Talanta 2008, 76(3):602-609.
7. Stanimirova I., Daszykowski M., Walczak B. Dealing with missing values and outliers in principal component analysis. Talanta 2007, 72(1):172-178.
8. Serneels S., Verdonck T. Principal component analysis for data containing outliers and missing elements. Comput. Statist. Data Anal. 2008, 52(3):1712-1727.
9. S. Rässler, The Impact of Multiple Imputation for DACSEIS, Technical Report DACSEIS Research Paper Series 5, University of Erlangen-Nürnberg, Nürnberg, Germany, 2004.
10. Williams D., Liao X., Xue Y., Carin L., Krishnaouram B. On classification with incomplete data. IEEE Trans. Pattern Anal. Mach. Intell. 2007, 29(3):427-436.
11. Little R.J.A., Rubin D.B. Statistical Analysis with Missing Data 1987, Wiley, New York.
12. Ramoni M., Sebastiani P. Robust Bayes classifiers. Artif. Intell. 2001, 125(1-2):209-226.
13. Ramoni M., Sebastiani P. Robust learning with missing data. Mach. Learn. 2001, 45(2):147-170.
14. J.N. Chen, X.P. Xue, F.Z. Tian, H.K. Huang, An algorithm for classifying incomplete data with selective Bayes classifiers, in: Proceedings of the 2007 International Conference on Computational Intelligence and Security Workshops, Harbin, Heilongjiang, China, December 15-19 2007, pp. 445-448.
15. Winston P.H. Artificial Intelligence 1992, Addison-Wesley, Reading, MA.
16. Czekaj T., Wu W., Walczak B. Classification of genomic data: some aspects of feature selection. Talanta 2008, 76(3):564-574.
17. Brown M., Costen N.P. Exploratory basis pursuit classification. Pattern Recognition Lett. 2005, 26(12):1907-1915.
18. Mounach A., Mouinga Abayi D.A., Ghazi M., Ghozlani I., Nouijai A., Achemlal L., Bezza A., El Maghraoui A. Discordance between hip and spine bone mineral density measurement using DXA: prevalence and risk factors. Semin. Arthritis Rheum. 2009, 38(6):467-471.
19. Ramanujam P., Castillo E., Patel E., Vilke G., WilSon M.P., Dunford J.V. Prehospital transport time intervals for acute stroke patients. J. Emerg. Med. 2009, 37(1):40-45.
20. Lee S.Y., Song X.Y. Maximum likelihood estimation and model comparison for mixtures of structural equation models with ignorable missing data. J. Classif. 2003, 20(2):221-255.
21. Clark P., Niblett T. The CN2 induction algorithm. Mach. Learn. 1989, 3(4):261-283.
22. Wasito I., Mirkin B. Nearest neighbor approach in the least-squares data imputation algorithms. Inf. Sci. 2005, 169(1-2):1-25.
23. Dries A., de Raedt L., Nijssen S. Mining predictive k-CNF expressions. IEEE Trans. Knowl. Data Eng. 2010, 22(5):743-748.
24. Gheyas I.A., Smith L.S. A neural network-based framework for the reconstruction of incomplete data sets. Neurocomputing 2010, 73(16-18):3039-3065.
25. Samad T., Harp S.A. Self-organization with partial data. Network-Comput. Neural Syst. 1992, 3(2):205-212.
26. Wang S.H. Application of self-organising maps for data mining with incomplete data sets. Neural Comput. Appl. 2003, 12(1):42-48.
27. Merlin P., Sorjamaa A., Maillet B., Lendasse A. X-SOM and L-SOM: a double classification approach for missing value imputation. Neurocomputing 2010, 73(7-9):1103-1108.
28. I. Kononenko, I. Bratko, E. Roskar, Experiments in Automatic Learning of Medical Diagnostic Rules, Technical Report, Jozef Stefan Institute, Lljubljana, Yugoslavia, 1984.
29. McLachalan G.J., Krishnan T. The EM Algorithm and Extensions 1997, John Wiley & Sons, New York.
30. Fernandez-Alonso R., Suarez-Alvarez J. Imputation methods for missing data in education diagnostic evaluation. Psicothema 2012, 24(1):167-175.
31. Theobald D.L., Steindel P.A. Optimal simultaneous superpositioning of multiple structures with missing data. Bioinformatics 2012, 28(15):1972-1979.
32. Birbil Ş.İ., Fang S.C. An electromagnetism-like mechanism for global optimization. J. Global Optim. 2003, 25(3):263-282.
33. Su C.T., Lin H.C. Applying electromagnetism-like mechanism for feature selection. Inf. Sci. 2011, 181(5):972-986.
34. Utgoff P.E. Incremental induction of decision trees. Mach. Learn. 1989, 4(2):161-186.
35. T.P. Hong, S.S. Tseng, Models of parallel learning systems, in: Proceeding of 11th IEEE International Conference on Distributed Computing Systems, Arlington, Texas, USA, May 20-24 1991, pp. 125-132.
36. Chen J.N., Huang H.K., Tian F.Z., Tian S.F. A selective Bayes classifier for classifying incomplete data based on gain ratio. Knowl.-Based Syst. 2008, 21(7):530-534.
37. Tirumalai V., Ricks K.G., Woodbury K.A. Using parallelization and hardware concurrency to improve the performance of a genetic algorithm. Concurrency Comput.: Pract. Exper. 2007, 19(4):443-462.