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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Akil, H., Martone, M.E., Van Essen, D.C.: Challenges and Opportunities in Mining Neuroscience Data. Science 331(6018), 708–712 (2011)
Bleiholder, J., Naumann, F.: Data fusion. ACM Computing Surveys 41(1), 1–41 (2008)
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)
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)
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)
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)
Dasarathy, B.V.: Elucidative fusion systems—an exposition. Information Fusion 1, 5–15 (2000)
Dowling, J.E.: Neurons and Networks: An Introduction to Behavioral Neuroscience, 2nd edn. Belknap Press of Harvard University Press, Cambridge (2001)
Engle, R.: Anticipating Correlations: A New Paradigm for Risk Management. Princeton University Press, Princeton (2009)
Fleming, S.M., et al.: Relating introspective accuracy to individual differences in brain structure. Science 329, 1541–1543 (2010)
Gewin, V.: Rack and Field. Nature 460, 944–946 (2009)
Gold, J.I., Shadlen, M.N.: The neural basis of decision making. Annual Review of Neuroscience 30, 535–574 (2007)
Green, D.M., Swets, J.A.: Signal Detection Theory and Psychophysics. John Wiley & Sons, New York (1966)
Hey, T., et al. (eds.): Jim Gray on eScience: A Transformed Scientific Method, in the Fourth Paradigm. Microsoft Research, pp.17–31 (2009)
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)
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)
Hsu, D.F., Taksa, I.: Comparing rank and score combination methods for data fusion in information retrieval. Information Retrieval 8(3), 449–480 (2005)
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)
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)
Kiani, R., Shadlen, M.N.: Representation of confidence associated with a decision by neurons in the parietal cortex. Science 324, 759–764 (2009)
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)
Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience, Hoboken (2004)
Lau, H., Maniscalco, B.: Should confidence be trusted? Science 329, 1478–1479 (2010)
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)
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)
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)
Lyons, D.M., Hsu, D.F.: Combining multiple scoring systems for target tracking using rank-score characteristics. Information Fusion 10(2), 124–136 (2009)
Macmillan, N.A., Creelman, C.D.: Detection Theory: A User’s Guide, 2nd edn. Psychology Press, New York (2005)
McMunn-Coffran, C., Schweikert, C., Hsu, D.F.: Microarray Gene Expression Analysis Using Combinatorial Fusion. In: BIBE, pp. 410–414 (2009)
Mesterharm, C., Hsu, D.F.: Combinatorial Fusion with On-line Learning Algorithms. In: The 11th International Conference on Information Fusion, pp. 1117–1124 (2008)
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)
Norvig, P.: Search. In ”2020 vision”. Nature 463, 26 (2010)
Ohshima, M., Zhong, N., Yao, Y., Liu, C.: Relational peculiarity-oriented mining. Data Min. Knowl. Disc. 15, 249–273 (2007)
Parker, A.J., Newsome, W.T.: Sense and the single neuron: Probing the physiology of perception. Annu. Rev. Neuroscience 21, 227–277 (1998)
Pawela, C., Biswal, B.: Brain Connectivity: A new journal emerges. Brain Connectivity 1(1), 1–2 (2011)
Rieke, F., Warland, D., de Ruyter van Steveninck, R., Bialek, W.: Spikes: Exploring the Neural Code. MIT Press, Cambridge (1997)
Schadt, E.: Molecular networks as sensors and drivers of common human diseases. Nature 461, 218–223 (2009)
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)
Sharkey, A.J.C. (ed.): Combining Artificial Neural Nets: Ensemble and. Modular Multi-Net Systems. Perspectives in Neural Computing. Springer, London (1999)
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)
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)
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)
Zhong, N., Yao, Y., Ohshima, M.: Peculiarity oriented multidatabase mining. IEEE Trans. Knowl. Data Eng. 15(4), 952–960 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hsu, D.F., Ito, T., Schweikert, C., Matsuda, T., Shimojo, S. (2011). Combinatorial Fusion Analysis in Brain Informatics: Gender Variation in Facial Attractiveness Judgment. In: Hu, B., Liu, J., Chen, L., Zhong, N. (eds) Brain Informatics. BI 2011. Lecture Notes in Computer Science(), vol 6889. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23605-1_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-23605-1_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23604-4
Online ISBN: 978-3-642-23605-1
eBook Packages: Computer ScienceComputer Science (R0)