Comparison of imputation methods for discriminant analysis with strategically hidden data
| Autor: | Juheng Zhang, Haldun Aytug |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Information Systems and Management
General Computer Science business.industry Computer science 05 social sciences 02 engineering and technology Management Science and Operations Research Missing data Information theory Linear discriminant analysis computer.software_genre Industrial and Manufacturing Engineering Hidden data Analytics Modeling and Simulation 0502 economics and business 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Imputation (statistics) Data mining 050207 economics business computer |
| Zdroj: | European Journal of Operational Research. 255:522-530 |
| ISSN: | 0377-2217 |
| DOI: | 10.1016/j.ejor.2016.05.052 |
| Popis: | In many situations, data may be selectively presented by data providers to achieve desirable but undeserved decision outcomes from decision makers. Decisions taken without considering strategic information revelation might be biased. We revisit and study the properties of two methods handling strategically missing data in a classification context. The asymptotic analysis suggests that when the training sets are sufficiently large these methods outperform the conventional methods handling missing data that do not consider strategic motivations of agents (e.g., Average method and Similarity method). Scale-up experiments support the theoretical findings and show that as the training size increases the misclassification rates of those methods decrease. We show that sampling can be used to efficiently identify sufficient information for the imputation methods to treat strategically missing data. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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