Abstract
In Combinatorial Fusion Analysis (CFA), a set of multiple scoring systems is used to facilitate integration and fusion of data, features, and/or decisions so as to improve the quality of resultant decisions and actions. Specifically, in a recently developed information fusion method, each system consists of a score function, a rank function, and a Rank-Score Characteristic (RSC) function. The RSC function illustrates the scoring (or ranking) behavior of the system. In this report, we show that RSC functions can be computed easily and RSC functions can be used to measure cognitive diversity for two or more scoring systems. In addition, we show that measuring diversity using the RSC function is inherently distinct from the concept of correlation in statistics and can be used to improve fusion results in classification and decision making. Among a set of domain applications, we discuss information retrieval, virtual screening, and target tracking.
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
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. MCS, 407–420 (2007)
Chung, Y.S., Hsu, D.F., Liu, C.Y., Tang, C.Y.: Performance Evaluation of Classifer Ensembles in Terms of Diversity and Performance of Individual Systems (submitted)
Dasarathy, B.V.: Elucidative fusion systems—an exposition. Information Fusion 1, 5–15 (2000)
Denning, P.J.: The profession of IT: The IT schools movement. Commun. ACM 44(8), 19–22 (2001)
Engle, R.: Anticipating Correlations: A New Paradigm for Risk Management. Princeton University Press, Princeton (2009)
Gewin, V.: Rack and Field. Nature 460, 944–946 (2009)
Hey, T., et al.(eds.): Jim Gray on eScience: A Transformed Scientific Method, in the Fourth Paradigm, pp. 17–31. Microsoft Research(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)
Krogh, A., Vedelsby, J.: Neural Network Ensembles, Cross Validation, and Active Learning. In: Advances in Neural Information Processing Systems, vol. 7, pp. 231–238. M.I.T. Press, Cambridge (1995)
Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience, Hoboken (2004)
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)
Lin, K.-L., et al.: Feature Selection and Combination Criteria for Improving Accuracy in Protein Structure Prediction. IEEE Transactions on Nanobioscience 6(2), 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)
McMunn-Coffran, C., Schweikert, C., Hsu, D.F.: Microarray Gene Expression Analysis Using Combinatorial Fusion. BIBE, 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 visions”. Nature 463, 26 (2010)
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. BIBE, 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. In: 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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hsu, D.F., Kristal, B.S., Schweikert, C. (2010). Rank-Score Characteristics (RSC) Function and Cognitive Diversity. In: Yao, Y., Sun, R., Poggio, T., Liu, J., Zhong, N., Huang, J. (eds) Brain Informatics. BI 2010. Lecture Notes in Computer Science(), vol 6334. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15314-3_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-15314-3_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15313-6
Online ISBN: 978-3-642-15314-3
eBook Packages: Computer ScienceComputer Science (R0)