Combinatorial fusion analysis: Methods and practices of combining multiple scoring systems

Advanced Data Mining Technologies in Bioinformatics

Volumn , Issue , 2006, Pages 32-62

  Hsu, D Frank ^a   Chung, Yun Sheng ^b   Kristal, Bruce S ^c,d  

^a FORDHAM UNIVERSITY   (United States)

^b NATIONAL TSING HUA UNIVERSITY   (Taiwan)

^c BURKE MEDICAL RESEARCH INSTITUTE   (United States)

^d WEILL CORNELL MEDICAL COLLEGE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84900240111     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.4018/978-1-59140-863-5.ch003     Document Type: Chapter

References (59)

1. Arrow, K.J. (1963). Social choices and individual values. New York: John Wiley.
2. Aslam, J.A, Pavlu, V., & Savell, R. (2003). A unified model for metasearch, pooling, and system evaluation. In O. Frieder (Ed.), Proceedings of the Twelfth International Conference on Information and Knowledge Management (pp. 484-491). New York: ACM Press.
3. Belkin, N.J., Kantor, P.B., Fox, E.A., & Shaw, J.A. (1995). Combining evidence of multiple query representation for information retrieval. Information Processing & Management, 31(3), 431-448.
4. Biggs, N.L., & White, T. (1979). Permutation groups and combinatorial structures (LMS Lecture Note Series, Vol. 33). Cambridge: Cambridge University Press.
5. Brown, G., Wyatt, J., Harris, R., & Yao, X. (2005). Diversity creation methods: a survey and categorization. Information Fusion, 6, 5-20.
6. Chuang, H.Y., Liu, H.F., Brown, S., McMunn-Coffran, C., Kao, C.Y., & Hsu, D.F. (2004). Identifying significant genes from microarray data. In Proceedings of IEEE BIBE'04 (pp. 358-365). IEEE Computer Society.
7. Chuang, H.Y., Liu, H.F., Chen, F.A., Kao, C.Y., & Hsu, D.F. (2004). Combination method in microarray analysis. In D.F. Hsu et al. (Ed.), Proceedings of the 7th International Symposium on Parallel Architectures, Algorithms and Networks (I-SPAN'04) (pp. 625-630). IEEE Computer Society.
8. Collins, R.T., Lipton, A.J., Fujiyoshi, H., & Kanade, T. (2001). Algorithms for cooperative multisensor surveillance. Proceedings of the IEEE, 89 (10), 1456-1477.
9. Corne, D.W., Deb, K., Fleming, P.J., & Knowles, J.D. (2003). The good of the many outweighs the good of the one: evolutional multi-objective optimization [Featured article]. IEEE Neural Networks Society, 9-13.
10. Dasarathy, B.V. (2000). Elucidative fusion systems-an exposition. Information Fusion, 1, 5-15.
11. Diligenti, M., Gori, M., & Maggini, M. (2004). A unified probabilistic framework for web page scoring systems, IEEE Trans. on Knowledge and Data Engineering, 16(1), 4-16.
12. Ding, C.H.Q., & Dubchak, I. (2001). Multi-class protein fold recognition using support vector machines and neural networks. Bioinformatics, 17 (4), 349-358.
13. Duerr, B., Haettich, W., Tropf, H., & Winkler, G. (1980). A combination of statistical and syntactical pattern recognition applied to classification of unconstrained handwritten numerals. Pattern Recognition, 12(3), 189-199.
14. Dwork, C., Kumar, R., Naor, M., & Sivakumar, D. (2001). Rank aggregation methods for the web. In Proceedings of the Tenth International World Wide Web Conference, WWW 10 (pp. 613-622). New York: ACM Press.
15. Fagin, R., Kumar, R., & Sivakumar, D. (2003). Comparing top k-lists. SIAM Journal on Discrete Mathematics, 17, 134-160.
16. Freund, Y., Iyer, R., Schapire, R.E., & Singer, Y. (2003). An efficient boosting algorithm for combining preferences. Journal of Machine Learning Research, 4, 933-969.
17. Garcia-Pedrajas, N., Hervas-Martinez, C., & Ortiz-Boyer, D. (2005). Cooperative coevolution of artificial neural network ensembles for pattern classification. IEEE Trans. on Evolutional Computation, 9(3), 271-302.
18. Ginn, C. M. R., Willett, P., & Bradshaw, J. (2000). Combination of molecular similarity measures using data fusion [Perspectives]. Drug Discovery and Design, 20, 1-15.
19. Grammatikakis, M.D., Hsu, D.F., & Kraetzl, M. (2001). Parallel system interconnections and communications. Boca Raton, FL: CRC Press.
20. Heydemann, M.C. (1997). Cayley graphs and interconnection networks. In G. Hahn & G. Sabidussi (Eds.), Graph symmetry (pp. 161-224). Norwell, MA: Kluwer Academic Publishers.
21. Ho, T.K. (2002). Multiple classifier combination: Lessons and next steps. In H. Bunke & A. Kandel (Ed.), Hybrid methods in pattern recognition (pp. 171-198). Singapore: World Scientific.
22. Ho, T.K., Hull, J.J., & Srihari, S.N. (1992). Combination of decisions by multiple classifiers. In H.S. Baird, H. Burke, & K. Yamulmoto (Eds.), Structured document image analysis (pp. 188-202). Berlin: Springer-Verlag.
23. Ho, T.K., Hull, J.J., & Srihari, S.N. (1994). Decision combination in multiple classifier system. IEEE Trans. on Pattern Analysis and Machine Intelligence, 16(1), 66-75.
24. Hsu, D.F., & Palumbo, A. (2004). A study of data fusion in Cayley graphs G(Sn, Pn). In: D.F. Hsu et al. (Ed.), Proceedings of the 7th International Symposium on Parallel Architectures, Algorithms and Networks (I-SPAN'04) (pp. 557-562). IEEE Computer Society.
25. Hsu, D.F., Shapiro, J., & Taksa, I. (2002). Methods of data fusion in information retrieval: Rank vs. score combination (Tech. Rep. 2002-58). Piscataway, NJ: DIMACS Center.
26. Hsu, D.F., & Taksa, I. (2005). Comparing rank and score combination methods for data fusion in information retrieval. Information Retrieval, 8(3), 449-480.
27. Hu, W., Tan, T., Wang, L., & Maybank, S. (2004). A survey on visual surveillance of object motion and behaviors. IEEE Trans. on Systems, Man, and Cybernetics- Part C: Applications and Review, 34(3), 334-352.
28. Huang, C.D., Lin, C.T., & Pal, N.R. (2003). Hierarchical learning architecture with automatic feature selection for multi-class protein fold classification. IEEE Trans. on NanoBioscience, 2(4), 503-517.
29. Ibraev, U., Ng, K.B., & Kantor, P.B. (2001). Counter intuitive cases of data fusion in information retrieval (Tech. Rep.). Rutgers University.
30. Jin, X., & Branke, J. (2005). Evolutional optimization in uncertain environments-a survey. IEEE Trans. on Evolutional Computation, 9(3), 303-317.
31. Kantor, P.B. (1998, Jan). Semantic dimension: On the effectiveness of naïve data fusion methods in certain learning and detection problems. Paper presented at the meeting of the Fifth International Symposium on Artificial Intelligence and Mathematics, Ft. Lauderdalee, FL.
32. Kendall, M., & Gibbons, J.D. (1990). Rank correlation methods. London: Edward Arnold.
33. Kittler, J., & Alkoot, F.M. (2003). Sum versus vote fusion in multiple classifier systems. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(1), 110-115.
34. Kuncheva, L.I. (2005). Diversity in multiple classifier systems [Guest editorial]. Information Fusion, 6, 3-4.
35. Kuriakose, M.A., Chen, W.T., He, Z.M., Sikora, A.G., Zhang, P., Zhang, Z.Y., Qiu, W.L., Hsu, D.F., McMunn-Coffran, C., Brown, S.M., Elango, E.M., Delacure, M.D., & Chen, F.A. (2004). Selection and Validation of differentially expressed genes in head and neck cancer. Cellular and Molecular Life Sciences, 61, 1372-1383.
36. Lee, J.H. (1997). Analyses of multiple evidence combination. In N.J. Belkin, A.D. Narasimhalu, P. Willett, W. Hersh (Ed.), Proceedings of the 20th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 267-276). New York: ACM Press.
37. Lin, C.-Y., Lin, K.-L., Huang, C.-D., Chang, H.-M., Yang, C.-Y., Lin, C.-T., & Hsu, D.F. (2005). Feature selection and combination criteria for improving predictive accuracy in protein structure classification. In Proceedings of IEEE BIBE'05 (pp. 311315). IEEE Computer Society.
38. Lin, K.-L., Lin, C.-Y., Huang, C.-D., Chang, H.-M., Lin, C.-T., Tang, C.Y., & Hsu, D.F. (2005a). Improving prediction accuracy for protein structure classification by neural networks using feature combination. Proceedings of the 5th WSEAS International Conference on Applied Informatics and Communications (AIC'05)(pp. 313-318).
39. Lin, K.-L., Lin, C.Y., Huang, C.-D., Chang, H.-M., Lin, C.-T., Tang, C.Y., & Hsu, D.F. (2005b). Methods of improving protein structure prediction based on HLA neural networks and combinatorial fusion analysis. WSEAS Trans. on Information Science and Application, 2, 2146-2153.
40. Marden, J.I. (1995). Analyzing and modeling rank data. (Monographs on Statistics and Applied Probability, No. 64). London: Chapman & Hall.
41. McCullagh, P. (1992). Models on spheres and models for permutations. In Probabality Models and Statistical Analysis for Ranking Data (M.A. Fligner, & J.S. Verducci, Ed., pp. 278-283). (Lecture Notes in Statistics, No. 80). Berlin: Springer-Verlag.
42. Melnik, D., Vardi, Y., & Zhang, C.U. (2004). Mixed group ranks: preference and confidence in classifier combination. IEEE Trans. on Pattern Analysis and Machine Intelligence, 26(8), 973-981.
43. Ng, K.B., & Kantor, P.B. (1998). An investigation of the preconditions for effective data fusion in information retrieval: A pilot study. In C.M. Preston (Ed.), Proceedings of the 61st Annual Meeting of the American Society for Information Science (pp. 166-178). Medford, NJ: Information Today.
44. Ng, K.B., & Kantor, P.B. (2000). Predicting the effectiveness of naïve data fusion on the basis of system characteristics. Journal of the American Society for Information Science. 51(13), 1177-1189.
45. Paolucci, U., Vigneau-Callahan, K. E., Shi, H., Matson, W. R., & Kristal, B. S. (2004). Development of biomarkers based on diet-dependent metabolic serotypes: Characteristics of component-based models of metabolic serotype. OMICS, 8, 221-238.
46. Patil, G.P., & Taillie, C. (2004). Multiple indicators, partially ordered sets, and linear extensions: multi-criterion ranking and prioritization. Environmental and Ecological Statistics, 11, 199-288.
47. Perrone, M.P., & Cooper, L.N. (1992). When networks disagree: Ensemble methods for hybrid neural networks (Report AF-S260 045). U.S. Dept. of Commerce.
48. Shi, H., Paolucci, U., Vigneau-Callahan, K. E., Milbury, P. E., Matson, W. R., & Kristal, B. S. (2004). Development of biomarkers based on diet-dependent metabolic serotypes: Practical issues in development of expert system-based classification models in metabolomic studies. OMICS, 8, 197-208.
49. Shi, H., Vigneau-Callahan, K., Shestopalov, I., Milbury, P.E., Matson, W.R., & Kristal B.S. (2002a). Characterization of diet-dependent metabolic serotypes: Proof of principle in female and male rats. The Journal of Nutrition, 132, 1031-1038.
50. Shi, H., Vigneau-Callahan, K., Shestopalov, I., Milbury, P.E., Matson, W.R., & Kristal, B.S. (2002b). Characterization of diet-dependent metabolic serotypes: Primary validation of male and female serotypes in independent cohorts of rats. The Journal of Nutrition, 132, 1039-1046.
51. Shoichet, B.K (2004). Virtual screening of chemical libraries. Nature, 432, 862-865.
52. Thompson, G. L. (1992). Graphical techniques for ranked data. In Probabality Models and Statistical Analysis for Ranking Data (M.A. Fligner, & J.S. Verducci, Ed., pp. 294-298). (Lecture Notes in Statistics, No. 80). Berlin: Springer-Verlag.
53. Triesch, J., & von der Malsburg, C. (2001). Democratic integration: self-organized integration of adaptive cues. Neural Computation, 13, 2049-2074.
54. Tumer, K., & Ghosh, J. (1999). Linear and order statistics combinations for pattern classification. In: Amanda Sharkey (Ed.), Combining artificial neural networks (pp. 127-162). Berlin: Springer-Verlag.
55. Vogt, C. C., & Cottrell, G. W. (1999). Fusion via a linear combination of scores. Information Retrieval, 1(3), 151-173.
56. Xu, L., Krzyzak, A., & Suen, C.Y. (1992). Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Transactions on Systems, Man, and Cybernetics, 22(3), 418-435.
57. Yang, J.M., Chen, Y.F., Shen, T.W., Kristal, B.S., & Hsu, D.F. (2005). Consensus scoring for improving enrichment in virtual screening. Journal of Chemical Information and Modeling, 45, 1134-1146.
58. Young, H.P. (1975). Social choice scoring functions. SIAM Journal on Applied Mathematics, 28(4), 824-838.
59. Young, H. P., & Levenglick, A. (1978). A consistent extension of Condorcet's election principle. SIAM Journal on Applied Mathematics, 35(2), 285-300.