J.T.: Rough set model selection for practical decision making (2007)
| Venue: | In: Proceeding of Fuzzy Systems and Knowledge Discovery (FSKD’07). III |
| Citations: | 5 - 4 self |
BibTeX
@INPROCEEDINGS{Herbert07j.t.:rough,
author = {Joseph P. Herbert and Jingtao Yao},
title = {J.T.: Rough set model selection for practical decision making},
booktitle = {In: Proceeding of Fuzzy Systems and Knowledge Discovery (FSKD’07). III},
year = {2007},
pages = {203--207}
}
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Abstract
One of the challenges a decision maker faces is choosing a suitable rough set model to use for data analysis. The traditional algebraic rough set model classifies objects into three regions, namely, the positive, negative, and boundary regions. Two different probabilistic models, variableprecision and decision-theoretic, modify these regions via l,u user-defined thresholds and α, β values from loss functions respectively. A decision maker whom uses these models must know what type of decisions can be made within these regions. This will allow him or her to conclude which model is best for their decision needs. We present an outline that can be used to select a model and better analyze the consequences and outcomes of those decisions. 1.
