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Full-Text Articles in Management Information Systems
An Adaptive Learning Model Which Accommodates Asymmetric Error Costs And Choice-Based Samples, James V. Hansen, James B. Mcdonald, Rayman D. Meservy
An Adaptive Learning Model Which Accommodates Asymmetric Error Costs And Choice-Based Samples, James V. Hansen, James B. Mcdonald, Rayman D. Meservy
Faculty Publications
This paper introduces an adaptive-learning model, EGB2, which optimizes over a parameter space to fit data to a family of models based on maximum-likelihood criteria. We also show how EGB2 can be modified to handle asymmetric costs of Type I and Type II errors, thereby minimizing misclassification costs. It has been shown that standard methods of computing maximum-likelihood estimators of qualitative-response models are generally inconsistent when applied to sample data with different proportions than found in the universe from which the sample is drawn. We investigate how a choice estimator, based on weighting each observation's contribution to the log-likelihood function, …
Exceptions And Exception Handling In Computerized Information Processes, Diane M. Strong, Steven M. Miller
Exceptions And Exception Handling In Computerized Information Processes, Diane M. Strong, Steven M. Miller
Research Collection School Of Computing and Information Systems
Exceptions, situations that cannot be correctly processed by computer systems, occur frequently in computer-based information processes. Five perspectives on exceptions provide insights into why exceptions occur and how they might be eliminated or more efficiently handled. We investigate these perspectives using an in-depth study of an operating information process that has frequent exceptions. Our results support the use of a total quality management (TQM) approach of eliminating exceptions for some exceptions, in particular, those caused by computer systems that are poor matches to organizational processes. However, some exceptions are explained better by a political system perspective of conflicting goals between …
Case-Based Reasoning: Application Techniques For Decision Support, James V. Hansen, Rayman D. Meservy, Larry E. Wood
Case-Based Reasoning: Application Techniques For Decision Support, James V. Hansen, Rayman D. Meservy, Larry E. Wood
Faculty Publications
Decision-support systems can be improved by enabling them to use past decisions to assist in making present ones. Reasoning from relevant past cases is appealing because it corresponds to some of the processes an expert uses to solve problems quickly and accurately. All this depends on an effective method of organizing cases for retrieval. This paper investigates the use of inductive networks as a means for case organization and outlines an approach to determining the desired number of cases-or assessing the reliability of a given number. Our method is demonstrated by application to decision making on corporate tax audits.