Constructing Ensembles from Data Envelopment Analysis
Published Online:20 Jul 2007https://doi.org/10.1287/ijoc.1060.0180
References
- Error reduction through learning multiple descriptions. Machine Learn. (1996) 24:173–202Crossref, Google Scholar
- A procedure for ranking efficient units in data envelopment analysis. Management Sci. (1993) 39:1261–264Link, Google Scholar
- Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Sci. (1984) 30:1078–1092Link, Google Scholar
- The quickhull algorithm for convex hulls. ACM Trans. Math. Software (1996) 22:469–483Crossref, Google Scholar
- An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learn. (1999) 36:105–142Crossref, Google Scholar
- UCI repository of machine learning database. (1998) . Technical report, Department of Information and Computer Science, University of California, Irvine, CAGoogle Scholar
- Bagging predictors. Machine Learn. (1996a) 24:123–140Crossref, Google Scholar
- Stacked regressions. Machine Learn. (1996b) 24:49–64Crossref, Google Scholar
- Arcing classifiers. Ann. Statist. (1998) 26:801–849Crossref, Google Scholar
- Random forests. Machine Learn. (2001) 45:5–32Crossref, Google Scholar
- Measuring the efficiency of decision making units. Eur. J. Oper. Res. (1978) 2:429–444Crossref, Google Scholar
- Data Envelopment Analysis, Theory, Methodology and Applications (1997) (Kluwer Academic Publishers, Dordrecht, The Netherlands) Google Scholar
- Combining forecasts: A review and annoted bibliography. Internat. J. Forecasting (1989) 5:559–583Crossref, Google Scholar
- A guide to DEAP version 2.1: A data envelopment analysis (computer) program. (1996) . Technical report, Department of Operations Research, University of New England, Armidale, New South Wales, AustraliaGoogle Scholar
- Handbook on Data Envelopment Analysis (2004) (Kluwer Academic Publishers, Boston, MA) Crossref, Google Scholar
- Machine learning research: Four current directions. AI Magazine (1997) 18:97–136Google Scholar
- Ensemble methods in machine learning. Multiple Classifier Systems (2000) 18:1–15Crossref, Google Scholar
- Knowledge discovery via multiple models. Intelligent Data Anal. (1998) 2:187–202Crossref, Google Scholar
- Production Frontiers (1994) (Cambridge University Press, West Nyack, NY) Google Scholar
- Experiments with a new boosting algorithm. Proc. Thirteenth National Conf. Machine Learn. (1996) 148–156Google Scholar
- Anticipating the consequences of school reform: A new use of DEA. Management Sci. (1999) 45:608–620Link, Google Scholar
- Neural network ensembles. IEEE Trans. Pattern Anal. Machine Intelligence (1990) 12:993–1001Crossref, Google Scholar
- Bayesian model selection in finite mixtures by marginal density decompositions. J. Amer. Statist. Assoc. (2001) 96:1316–1332Crossref, Google Scholar
- A comparative assessment of classification methods. Decision Support Systems (2003) 35:441–454Crossref, Google Scholar
- Combination of multiple classifiers for the customer's purchase behavior prediction. Decision Support Systems (2003) 34:167–175Crossref, Google Scholar
- Convex hull machine for regression and classification. IEEE Conf. Data Mining (2002) Maebashi City, Japan:243–253Google Scholar
- Multiple classifier systems. Proc. 2nd Internat. Workshop on MCS (2001) Cambridge, UK(Springer-Verlag, Berlin, Germany) Crossref, Google Scholar
- Combining Pattern Classifiers: Methods and Algorithms (2004) (Wiley, New York) Crossref, Google Scholar
- Measures of diversity in classifier ensembles. Machine Learn. (2003) 51:181–207Crossref, Google Scholar
- Position paper: Extensions of ROC analysis to multi-class domains. Proc. ICML-2000 Workshop on Cost-Sensitive Learn. (2000) (Stanford University, Palo Alto, CA) Google Scholar
- Combining estimates in regression and classification. J. Amer. Statist. Assoc. (1996) 9:1641–1650Google Scholar
- Mathematical programming in data mining. Data Mining and Knowledge Discovery J. (1997) 1:183–210Crossref, Google Scholar
- Technological forecasting: Model selection, model stability, and combining models. Management Sci. (1998) 44:1115–1130Link, Google Scholar
- Combining classifiers via discretization. J. Amer. Statist. Assoc. (1999) 94:600–609Crossref, Google Scholar
- How many forecasters do you really have?: Mahalanobis provides the intuition for the surprising Clemens and Winkler result. Oper. Res. (1991) 39:519–523Link, Google Scholar
- Scalable optimization-based feature selection. Proc. SIAM Workshop Discrete Math. Data Mining (2002) Arlington, VA:53–64Google Scholar
- Popular ensemble methods: An empirical study. J. Artificial Intelligence Res. (1999) 11:169–198Crossref, Google Scholar
- On the use of optimization for data mining: Theoretical interactions and eCRM opportunities. Management Sci. (2003) 49:1327–1342Link, Google Scholar
- Leveraging the strengths of choice models and neural networks: A multi-product comparative analysis. Decision Sci. (2002) 33:433–468Crossref, Google Scholar
- Robust classification for imprecise environment. Machine Learn. (2001) 42:203–231Crossref, Google Scholar
- An empirical evaluation of alternative forecasting combinations. Management Sci. (1987) 33:1267–1276Link, Google Scholar
- Model selection criteria: An investigation of relative accuracy, posterior probabilities, and combination of criteria. Management Sci. (1995) 41:322–333Link, Google Scholar
- Profitability and marketability of the top 55 U.S. commercial banks. Management Sci. (1999) 45:1270–1288Link, Google Scholar
- Operations, quality, and profitability in the provision of banking services. Management Sci. (1999) 45:1221–1238Link, Google Scholar
- Note on the location of optimal classifiers in n-dimensional ROC space. (1999) . Technical report, Department of Computer Science, Oxford University, Oxford, UKGoogle Scholar
- Ensembles of learning machines. Lecture Notes in Computer Sciences (2002) 2486(Springer-Verlag, Berlin) 3–19Crossref, Google Scholar
- The combination of forecasts. J. Roy. Statist. Soc. (1983) 146:150–157Crossref, Google Scholar
- Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations (1999) (Morgan Kaufman, San Francisco, CA) Google Scholar
- No free lunch theorems for optimization. IEEE Trans. Evolutionary Comput. (1997) 1:67–82Crossref, Google Scholar
- Customer efficiency: Concept and its impact on e-business management. J. Service Res. (2002) 4:253–267Crossref, Google Scholar
- A Bayesian framework for the combination of classifier outputs. J. Oper. Res. Soc. (2002) 53:719–727Crossref, Google Scholar

