Advertisement

Combinatorial Fusion Analysis in Brain Informatics: Gender Variation in Facial Attractiveness Judgment

  • D. Frank Hsu
  • Takehito Ito
  • Christina Schweikert
  • Tetsuya Matsuda
  • Shinsuke Shimojo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6889)

Abstract

Information processing in the brain or other decision making systems, such as in multimedia, involves fusion of information from multiple sensors, sources, and systems at the data, feature or decision level. Combinatorial Fusion Analysis (CFA), a recently developed information fusion paradigm, uses a combinatorial method to model the decision space and the Rank-Score Characteristic (RSC) function to measure cognitive diversity. In this paper, we first introduce CFA and its practice in a variety of application domains such as computer vision and target tracking, information retrieval and Internet search, and virtual screening and drug discovery. We then apply CFA to investigate gender variation in facial attractiveness judgment on three tasks: liking, beauty and mentalization using RSC function. It is demonstrated that the RSC function is useful in the differentiation of gender variation and task judgment, and hence can be used to complement the notion of correlation which is widely used in statistical decision making. In addition, it is shown that CFA is a viable approach to deal with various issues and problems in brain informatics.

Keywords

Virtual Screening Rank Function Target Tracking Information Fusion Cognitive Diversity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Akil, H., Martone, M.E., Van Essen, D.C.: Challenges and Opportunities in Mining Neuroscience Data. Science 331(6018), 708–712 (2011)CrossRefGoogle Scholar
  2. 2.
    Bleiholder, J., Naumann, F.: Data fusion. ACM Computing Surveys 41(1), 1–41 (2008)CrossRefGoogle Scholar
  3. 3.
    Brown, G., Wyatt, J.L., Harris, R., Yao, X.: Diversity creation methods: A survey and categorisation. Journal of Information Fusion 6(1), 5–20 (2005a)CrossRefGoogle Scholar
  4. 4.
    Chun, Y.S., Hsu, D.F., Tang, C.Y.: On the relationships among various diversity measures in multiple classifier systems. In: 2008 International Symposium on Parallel Architectures, Algorithms, and Networks (ISPAN 2008), pp. 184–190 (2008)Google Scholar
  5. 5.
    Chung, Y. S., Hsu, D.F., Tang, C.Y.: On the diversity-performance relationship for majority voting in classifier ensembles. In: Haindl, M., Kittler, J., Roli, F. (eds.) MCS 2007. LNCS, vol. 4472, pp. 407–420. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Chung, Y.S., Hsu, D.F., Liu, C.Y., Tang, C.Y.: Performance evaluation of classifier ensembles in terms of diversity and performance of individual systems. Inter. Journal of Pervasive Computing and Communications 6(4), 373–403 (2010)CrossRefGoogle Scholar
  7. 7.
    Dasarathy, B.V.: Elucidative fusion systems—an exposition. Information Fusion 1, 5–15 (2000)CrossRefGoogle Scholar
  8. 8.
    Dowling, J.E.: Neurons and Networks: An Introduction to Behavioral Neuroscience, 2nd edn. Belknap Press of Harvard University Press, Cambridge (2001)Google Scholar
  9. 9.
    Engle, R.: Anticipating Correlations: A New Paradigm for Risk Management. Princeton University Press, Princeton (2009)Google Scholar
  10. 10.
    Fleming, S.M., et al.: Relating introspective accuracy to individual differences in brain structure. Science 329, 1541–1543 (2010)CrossRefGoogle Scholar
  11. 11.
    Gewin, V.: Rack and Field. Nature 460, 944–946 (2009)CrossRefGoogle Scholar
  12. 12.
    Gold, J.I., Shadlen, M.N.: The neural basis of decision making. Annual Review of Neuroscience 30, 535–574 (2007)CrossRefGoogle Scholar
  13. 13.
    Green, D.M., Swets, J.A.: Signal Detection Theory and Psychophysics. John Wiley & Sons, New York (1966)Google Scholar
  14. 14.
    Hey, T., et al. (eds.): Jim Gray on eScience: A Transformed Scientific Method, in the Fourth Paradigm. Microsoft Research, pp.17–31 (2009)Google Scholar
  15. 15.
    Ho, T.K.: Multiple classifier combination: Lessons and next steps. In: Bunke, H., Kandel, A. (eds.) Hybrid Methods in Pattern Recognition, pp. 171–198. World Scientific, Singapore (2002)CrossRefGoogle Scholar
  16. 16.
    Ho, T.K., Hull, J.J., Srihari, S.N.: Decision combination in multiple classifier system. IEEE Trans. on Pattern Analysis and Machine Intelligence 16(1), 66–75 (1994)CrossRefGoogle Scholar
  17. 17.
    Hsu, D.F., Taksa, I.: Comparing rank and score combination methods for data fusion in information retrieval. Information Retrieval 8(3), 449–480 (2005)CrossRefGoogle Scholar
  18. 18.
    Hsu, D.F., Chung, Y.S., Kristal, B.S.: Combinatorial fusion analysis: methods and practice of combining multiple scoring systems. In: Hsu, H.H. (ed.) Advanced Data Mining Technologies in Bioinformatics. Idea Group Inc., USA (2006)CrossRefGoogle Scholar
  19. 19.
    Hsu, D.F., Kristal, B.S., Schweikert, C.: Rank-Score Characteristics (RSC) Function and Cognitive Diversity. In: Yao, Y., Sun, R., Poggio, T., Liu, J., Zhong, N., Huang, J. (eds.) BI 2010. LNCS, vol. 6334, pp. 42–54. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  20. 20.
    Kiani, R., Shadlen, M.N.: Representation of confidence associated with a decision by neurons in the parietal cortex. Science 324, 759–764 (2009)CrossRefGoogle Scholar
  21. 21.
    Krogh, A., Vedelsby, J.: Neural Network Ensembles, Cross Validation, and Active Learning. In: Advances in Neural Information Processing Systems, pp. 231–238. M.I.T. Press, Cambridge (1995)Google Scholar
  22. 22.
    Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience, Hoboken (2004)CrossRefzbMATHGoogle Scholar
  23. 23.
    Lau, H., Maniscalco, B.: Should confidence be trusted? Science 329, 1478–1479 (2010)CrossRefGoogle Scholar
  24. 24.
    Li, Y., Hsu, D.F., Chung, S.M.: Combining Multiple Feature Selection Methods for Text Categorization by Using Rank-Score Characteristics. In: 21st IEEE International Conference on Tools with Artificial Intelligence, pp. 508–517 (2009)Google Scholar
  25. 25.
    Li, Y., Shi, N., Hsu, D.F.: Fusion Analysis of Information Retrieval Models on Biomedical Collections. In: 14th International Conference on Information Fusion, Fusion 2011 (July 2011) Google Scholar
  26. 26.
    Lin, K.-L., et al.: Feature Selection and Combination Criteria for Improving Accuracy in Protein Structure Prediction. IEEE Transactions on Nanobioscience 6, 186–196 (2007)CrossRefGoogle Scholar
  27. 27.
    Lyons, D.M., Hsu, D.F.: Combining multiple scoring systems for target tracking using rank-score characteristics. Information Fusion 10(2), 124–136 (2009)CrossRefGoogle Scholar
  28. 28.
    Macmillan, N.A., Creelman, C.D.: Detection Theory: A User’s Guide, 2nd edn. Psychology Press, New York (2005)Google Scholar
  29. 29.
    McMunn-Coffran, C., Schweikert, C., Hsu, D.F.: Microarray Gene Expression Analysis Using Combinatorial Fusion. In: BIBE, pp. 410–414 (2009)Google Scholar
  30. 30.
    Mesterharm, C., Hsu, D.F.: Combinatorial Fusion with On-line Learning Algorithms. In: The 11th International Conference on Information Fusion, pp. 1117–1124 (2008)Google Scholar
  31. 31.
    Ng, K.B., Kantor, P.B.: Predicting the effectiveness of naive data fusion on the basis of system characteristics. J. Am. Soc. Inform. Sci. 51(12), 1177–1189 (2000)CrossRefGoogle Scholar
  32. 32.
    Norvig, P.: Search. In ”2020 vision”. Nature 463, 26 (2010)CrossRefGoogle Scholar
  33. 33.
    Ohshima, M., Zhong, N., Yao, Y., Liu, C.: Relational peculiarity-oriented mining. Data Min. Knowl. Disc. 15, 249–273 (2007)MathSciNetCrossRefGoogle Scholar
  34. 34.
    Parker, A.J., Newsome, W.T.: Sense and the single neuron: Probing the physiology of perception. Annu. Rev. Neuroscience 21, 227–277 (1998)CrossRefGoogle Scholar
  35. 35.
    Pawela, C., Biswal, B.: Brain Connectivity: A new journal emerges. Brain Connectivity 1(1), 1–2 (2011)CrossRefGoogle Scholar
  36. 36.
    Rieke, F., Warland, D., de Ruyter van Steveninck, R., Bialek, W.: Spikes: Exploring the Neural Code. MIT Press, Cambridge (1997)zbMATHGoogle Scholar
  37. 37.
    Schadt, E.: Molecular networks as sensors and drivers of common human diseases. Nature 461, 218–223 (2009)CrossRefGoogle Scholar
  38. 38.
    Schweikert, C., Li, Y., Dayya, D., Yens, D., Torrents, M., Hsu, D.F.: Analysis of Autism Prevalence and Neurotoxins Using Combinatorial Fusion and Association Rule Mining. In: BIBE, pp. 400–404 (2009)Google Scholar
  39. 39.
    Sharkey, A.J.C. (ed.): Combining Artificial Neural Nets: Ensemble and. Modular Multi-Net Systems. Perspectives in Neural Computing. Springer, London (1999)zbMATHGoogle Scholar
  40. 40.
    Vinod, H.D., Hsu, D.F., Tian, Y.: Combinatorial Fusion for Improving Portfolio Performance. Advances in Social Science Research Using R, pp. 95–105. Springer, Heidelberg (2010)Google Scholar
  41. 41.
    Whittle, M., Gillet, V.J., Willett, P.: Analysis of data fusion methods in virtual screening: Theoretical model. Journal of Chemical Information and Modeling 46, 2193–2205 (2006)CrossRefGoogle Scholar
  42. 42.
    Yang, J.M., Chen, Y.F., Shen, T.W., Kristal, B.S., Hsu, D.F.: Consensus scoring for improving enrichment in virtual screening. Journal of Chemical Information and Modeling 45, 1134–1146 (2005)CrossRefGoogle Scholar
  43. 43.
    Zhong, N., Yao, Y., Ohshima, M.: Peculiarity oriented multidatabase mining. IEEE Trans. Knowl. Data Eng. 15(4), 952–960 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • D. Frank Hsu
    • 1
  • Takehito Ito
    • 2
  • Christina Schweikert
    • 1
  • Tetsuya Matsuda
    • 2
  • Shinsuke Shimojo
    • 3
  1. 1.Department of Computer and Information ScienceFordham UniversityNew YorkUSA
  2. 2.Tamagawa University Brain Science InstituteMachidaJapan
  3. 3.Division of Biology/Computation and Neural SystemsCalifornia Institute of TechnologyPasadenaUSA

Personalised recommendations