Information Market-Based Decision Fusion

Published Online:https://doi.org/10.1287/mnsc.1080.0977

References

  • Berg J. E., Rietz T. A. Prediction markets as decision support systems. Inform. Systems Frontiers (2003) 5(1):79–93CrossrefGoogle Scholar
  • Carlsson P., Ygge F., Andersson A. Extending equilibrium markets. IEEE Intelligent Systems (2001) 16(4):18–26CrossrefGoogle Scholar
  • Chan P. K., Fan W., Prodromidis A. L., Stolfo S. J. Distributed data mining in credit card fraud detection. IEEE Intelligent Systems Their Appl. (1999) 14(6):67–74CrossrefGoogle Scholar
  • Drummond C., Holte R. C. Cost curves: An improved method for visualizing classifier performance. Machine Learn. (2006) 65(1):95–130CrossrefGoogle Scholar
  • Duin P. W. R., Tax M. J. D. Experiments with classifier combining rules. Multiple Classifier Systems, Lecture Notes in Computer Science (2000) 1857(Springer, Berlin/Heidelberg) 16–29CrossrefGoogle Scholar
  • Fama E. Efficient capital markets: A review of theory and empirical work. J. Finance (1970) 25(2):383–417CrossrefGoogle Scholar
  • Fan J., Stolfo S., Zhang J. The application of AdaBoost for distributed, scalable and on-line learning. Proc. ACM SIGKDD 5th Internat. Conf. Knowledge Discovery and Data Mining (1999) (ACM, New York) 362–366CrossrefGoogle Scholar
  • Hanson R. Combinatorial information market design. Inform. Systems Frontiers (2003) 5(1):107–119CrossrefGoogle Scholar
  • Hayek F. A. The use of knowledge in society. Amer. Econom. Rev. (1945) 35(4):519–530Google Scholar
  • Jaccard J., Wan C. K.LISREL Approaches to Interaction Effects in Multiple Regression (1996) (Sage Publications, Thousand Oaks, CA) CrossrefGoogle Scholar
  • Jain A. K., Duin R. P. W., Mao J. Statistical pattern recognition: A review. IEEE Trans. Pattern Anal. Machine Intelligence (2000) 22(1):4–37CrossrefGoogle Scholar
  • Kelly J. A new interpretation of information rate. IEEE Trans. Inform. Theory (1956) 2(3):185–189CrossrefGoogle Scholar
  • Kennedy P. E. Estimation with correctly interpreted dummy variables in semilogarithmic equations. Amer. Econom. Rev. (1981) 71(4):801Google Scholar
  • Kittler J., Hatef M., Duin R. P. W., Matas J. On combining classifiers. IEEE Trans. Pattern Anal. Machine Intelligence (1998) 20(3):226–239CrossrefGoogle Scholar
  • Lam L. Classifier combinations: Implementations and theoretical issues. Multiple Classifier Systems, Lecture Notes in Computer Science (2000) 1857(Springer, Berlin/Heidelberg) 77–86CrossrefGoogle Scholar
  • Lee W., Stolfo S. J., Mok K. W. Adaptive intrusion detection: A data mining approach. Artificial Intelligence Rev. (2000) 14(6):533–567CrossrefGoogle Scholar
  • Lin J., Hwang M., Becker J. A fuzzy neural network for assessing the risk of fraudulent financial reporting. Managerial Auditing J. (2003) 18(8):657–665CrossrefGoogle Scholar
  • Newman D. J., Hettich S., Blake C. L., Merz C. J. UCI repository of machine learning databases. (1998) . http://www.ics.uci.edu/∼mlearn/MLRepository.htmlGoogle Scholar
  • Nissen M. E., Sengupta K. Incorporating software agents into supply chains: Experimental investigation with a procurement task. MIS Quart. (2006) 30(1):145–166CrossrefGoogle Scholar
  • Pennock M. D. A dynamic pari-mutuel market for hedging, wagering, and information aggregation. Proc. 5th ACM Conf. E-Commerce (2004) (ACM, New York) CrossrefGoogle Scholar
  • Plott C. R., Wit J., Yang W. C. Parimutuel betting markets as information aggregation devices: Experimental results. Econom. Theory (2003) 22(2):311–351CrossrefGoogle Scholar
  • Provost F., Fawcett T. Robust classification for imprecise environments. Machine Learn. (2001) 42(3):203–231CrossrefGoogle Scholar
  • Provost F., Fawcett T., Kohavi R. The case against accuracy estimation for comparing induction algorithms. Proc. 15th Internat. Conf. Machine Learn. (1998) (Morgan Kaufmann, San Francisco) 445–453Google Scholar
  • Rubinstein M. The strong case for the generalized logarithmic utility model as the premier model of financial markets. J. Finance (1976) 31(2):551–571CrossrefGoogle Scholar
  • Saar-Tsechansky M., Provost F. Active sampling for class probability estimation and ranking. Machine Learn. (2004) 54(2):153–178CrossrefGoogle Scholar
  • Stolfo S., Prodromidis A. L., Tselepis S., Lee W., Fan D. W., Chan P. K. JAM: Java agents for meta-learning over distributed databases. Proc. 3rd Internat. Conf. Knowledge Discovery and Data Mining (1997) (AAAI Press, Menlo Park, CA) 74–81Google Scholar
  • Suen C. Y., Lam L. Multiple classifier combination methodologies for different output levels. Multiple Classifier Systems, Lecture Notes in Computer Science (2000) 1857(Springer, Berlin/Heidelberg) 52–66CrossrefGoogle Scholar
  • Witten I. H., Frank E.Data Mining: Practical Machine Learning Tools and Techniques (2005) (Morgan Kaufmann, San Francisco) Google Scholar
  • Wolfers J., Zitzewitz E. Interpreting prediction market prices as probabilities. Proc. Allied Soc. Sci. Assoc. Annual Meeting (2006) January 6–8BostonCrossrefGoogle Scholar
  • Ygge F., Akkermans J. M. Decentralized markets versus central control: A comparative study. J. Artificial Intelligence Res. (1999) 11:301–333CrossrefGoogle Scholar
  • Yule G. U. On the association of attributes in statistics: With illustrations from the material of the childhood society, etc. Philos. Trans. Roy. Soc. London. Ser. A, Containing Papers Math. Physical Character (1900) 194:257–319CrossrefGoogle Scholar
  • Zheng Z., Padmanabhan B. Constructing ensembles from data envelopment analysis. INFORMS J. Comput. (2007) 19(4):486–496LinkGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.