A new method for attribute reduction to imcomplete decision table based on metric.
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DOI:
https://doi.org/10.15625/1813-9663/28/2/2494Abstract
In incomplete information systems, each subset of attributes determines a cover on the set of objects, in which each element is a tolerance class. Thus, a metric which is defined on the family of covers is established on the attribute sets. Once a metric is established, we can use the metric to measure attributes distance, cluster and discover important attributes. As a result, effective algorithms are constructed to solve attribute reduction in incomplete information systems.
With metric on the family of covers based on generalized Liang entropy, this paper proposes a new method for attribute reduction in incomplete decision table. The paper proves theoretically and experimentally that this metric method is more effective than other methods based on information quantity and tolerance matrix.
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