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Please use this identifier to cite or link to this item: http://hdl.handle.net/1860/1741

Title: Identifying critical factors in case-based prediction
Authors: Weber, Rosina O.
Evanco, William
Waller, Michael
Verner, June
Issue Date: 2004
Citation: Paper presented at the 17th Annual Conference of the International Florida Artificial Intelligence Research Society, Menlo Park, CA. Retrieved July 20, 2007 from http://www.ischool.drexel.edu/faculty/rweber/pdf/flairs04.pdf.
Abstract: A reversible outcome is one that can be changed. For example, the failure of an ongoing project may be avoided if certain actions are taken, while an outcome such as the path of a hurricane cannot be changed under current knowledge. The major benefit of predicting reversible outcomes resides in the possibility to avoid unwanted results. For this purpose, it is necessary to identify contributing factors responsible for the outcome, which once modified, can steer the result to a desired outcome. Consequently, the incorporation of a method into a case-based reasoning system to identify contributing factors affecting an outcome can improve its usefulness. This paper compares different approaches, particularly the use of domain knowledge, with respect to their ability to identify sets of factors that reverse software development projects predicted to fail into a prediction of success.
URI: http://hdl.handle.net/1860/1741
Appears in Collections:Faculty Research and Publications (IST)

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