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Identifying critical factors in case-based prediction
Please use this identifier to cite or link to this item:
http://hdl.handle.net/1860/1741
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| 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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