Isotonic Separation
Published Online:1 Nov 2005https://doi.org/10.1287/ijoc.1030.0061
References
- Network Flows (1993) (Prentice-Hall, Englewood Cliffs, NJ) Google Scholar
- Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. J. Finance (1968) 23:589–609Crossref, Google Scholar
- Separate sample logistic discrimination. Biometrika (1972) 59:19–35Crossref, Google Scholar
- Statistical Inference Under Order Restrictions (1972) (John Wiley and Sons, New York) Google Scholar
- Robust linear programming discrimination of two linearly inseparable sets. Optim. Methods Software (1992) 1:23–34Crossref, Google Scholar
- Neural Networks for Pattern Recognition (1995) (Oxford University Press, Oxford) Crossref, Google Scholar
- Structure algorithms for partially ordered isotonic regression. J. Comput. Graphical Statist. (1994) 3:285–300Crossref, Google Scholar
- Feature selection via concave minimization and support vector machines. Proc. Fifteenth Internat. Conf. Machine Learning (1998) (Morgan Kaufmann, San Mateo, CA) 82–90Google Scholar
- Feature selection via mathematical programming. INFORMS J. Comput. (1998) 10:209–217Link, Google Scholar
- A tutorial on support vector machines for pattern recognition. Data Mining Knowledge Discovery (1998) 2:121–167Crossref, Google Scholar
- Artificial neural networks for cancer research: Outcome prediction. Seminars Surgical Ontology (1994) 10:73–79Crossref, Google Scholar
- Breast cancer diagnosis using an isotonic separation approach. (1998) . Working paper, Department of Information Systems and Operations Management, School of Management, The University of Texas at Dallas, Richardson, TXGoogle Scholar
- , David F. N. Some procedures connected with the logistic qualitative response curve. Research Papers in Statistics: Festschrift for J. Neyman (1966) (John Wiley and Sons, New York) 55–71Google Scholar
- International application of a new probability algorithm for the diagnosis of coronary artery disease. Amer. J. Cardiology (1989) 64:304–310Crossref, Google Scholar
- An algorithm for isotonic regression of two or more independent variables. Ann. Statist. (1982) 10:708–711Crossref, Google Scholar
- The use of multiple measurements in taxonomy problem. Ann. Eugenics (1936) 7:179–188Crossref, Google Scholar
- A linear programming approach to the discriminant problem. Decision Sci. (1981) 12:68–74Crossref, Google Scholar
- An algorithm for monotone regression with one or more independent variables. Biometrika (1970) 57:263–271Crossref, Google Scholar
- Models of incremental concept formation. Artificial Intelligence (1989) 40:11–61Crossref, Google Scholar
- Improved linear programming model for discriminant analysis. Decision Sci. (1990) 21:771–785Crossref, Google Scholar
- A genetic algorithm for classification by feature partitioning. Proc. Fifth Internat. Conf. Genetic Algorithms (1993) (Morgan Kaufmann, San Mateo, CA) 543–548Google Scholar
- Filtering objectionable Internet content. Proc. Twentieth Internat. Conf. Inform. Systems (1999) (Association for Information Systems, Atlanta, GA) 274–278Google Scholar
- , Young T. Y., Fu K. S. Feature selection and extraction. Handbook of Pattern Recognition and Image Processing (1986) (Academic Press, New York) 59–83Google Scholar
- New developments of learning vector quantization and the self-organizing map. Proc. 1992 Sympos. Neural Networks: Alliances and Perspectives in Senri (SYNAPSE'92) (1992) (Senri International Information Institute, Osaka, Japan) Google Scholar
- Self-Organizing Maps (1995) (Springer-Verlag, Heidelberg, Germany) Crossref, Google Scholar
- A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Machine Learn. (2000) 4:203–228Crossref, Google Scholar
- Feature selection via discretization. IEEE Trans. Knowledge Data Engrg. (1997) 9:742–645Google Scholar
- Linear and nonlinear separation of patterns by linear programming. Oper. Res. (1965) 13:455–461Link, Google Scholar
- Multisurface method of pattern separation. IEEE Trans. Inform. Theory (1968) IT-14:801–807Crossref, Google Scholar
- , Coleman T. F., Li Y. Pattern recognition via linear programming: Theory and application to medical diagnosis. Large-Scale Numerical Optimization (1990) (Society for Industrial and Applied Mathematics, Philadelphia, PA) 22–31Google Scholar
- Breast cancer diagnosis and prognosis via linear programming. Oper. Res. (1995) 43:570–577Link, Google Scholar
- On the effectiveness of receptors in recognition systems. IEEE Trans. Inform. Theory (1963) 9:11–17Crossref, Google Scholar
- UCI repository of machine learning databases. (1998) . Department of Information and Computer Sciences, University of California, Irvine, CAGoogle Scholar
- Multi-purpose incremental learning system AQ15 and its testing application to three medical domains. Proc. Fifth National Conf. Artificial Intelligence (AAAI-86) (1986) (AAAI Press, Menlo Park, CA) 1041–1045Google Scholar
- Monotone networks. Proc. Royal Soc. London (1960) 257A:192–212Google Scholar
- A system for induction of oblique decision trees. J. Artificial Intelligence Res. (1994) 2:1–32Crossref, Google Scholar
- Linear and Combinatorial Programming (1976) (John Wiley and Sons, New York) Google Scholar
- A DEA application for marginal cost assignment in certain case based expert systems. Proc. Third INFORMS Conf. Inform. Systems Tech (1998) Montreal, Canada:347–358Google Scholar
- Further research on feature selection and classification using genetic algorithms. Proc. fifth Internat. Conf. Genetic Algorithms (1993) (Morgan Kaufmann, San Mateo, CA) 557–564Google Scholar
- Induction to decision trees. Machine Learn. (1986) 1:81–106Crossref, Google Scholar
- C4.5: Programs for Machine Learning (1993) (Morgan Kaufmann, San Mateo, CA) Google Scholar
- Order Restricted Statistical Inference (1988) (John Wiley and Sons, New York) Google Scholar
- Disease prognosis with an isotonic prediction technique. Proc. Ninth Workshop Inform. Tech. Systems (1999) Charlotte, NC:26–31Google Scholar
- Firm bankruptcy prediction: Experimental comparison of isotonic separation and other classification approaches. IEEE Trans. (2004) . ForthcomingGoogle Scholar
- Exemplar-based learning: Theory and implementation. (1988) . Technical Report TR-10-88, Aiken Computation Laboratory, Center for Research in Computing Technology, Harvard University, Cambridge, MAGoogle Scholar
- Neural networks for prognosis in breast cancer. Physica Medica: Eur. J. Medical Phys. (1993) IX(Supp. 1):175–178Google Scholar
- Mathematical Programming: Structures and Algorithms (1979) (John Wiley and Sons, New York) Google Scholar
- A note on genetic algorithms for large-scale feature selection. Pattern Recognition Lett. (1989) 10:335–347Crossref, Google Scholar
- Pattern classifier design by linear programming. IEEE Trans. Comput. (1968) C-17:367–372Crossref, Google Scholar
- An inductive learning approach to prognostic prediction. Proc. Twelfth Internat. Conf. Machine Learn (1995) (Morgan Kaufmann, San Mateo, CA) 522–530Crossref, Google Scholar
- A strong polynomial minimum cost circulation algorithm. Combinatorica (1985) 5:247–255Crossref, Google Scholar
- A strong polynomial algorithm to solve combinatorial linear programs. Oper. Res. (1986) 34:250–256Link, Google Scholar
- Statistical Learning Theory (1998) (John Wiley and Sons, New York) Google Scholar
- Multisurface method of pattern separation for medical diagnosis applied to breast cytology. Proc. National Acad. Sci. USA (1990) 87:9193–9196Crossref, Google Scholar
- Inference from partial orders: Central bank independence and inflation. (1997) . Technical report, Department of Economics, Heriot-Watt University, Edinburgh, ScotlandGoogle Scholar

