Uncertainly measures of rough set prediction
Artificial Intelligence
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
A new rough sets model based on database systems
Fundamenta Informaticae - Special issue on the 9th international conference on rough sets, fuzzy sets, data mining and granular computing (RSFDGrC 2003)
Information-theoretic measures of uncertainty for rough sets and rough relational databases
Information Sciences: an International Journal
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The information entropy, as a measurement of the average amount of information contained in an information system, is used in the classification of objects and the analysis of information systems. The information entropy of a partition is non-increasing when the partition is refined, and is related to rough sets by Wong and Ziarko. The partitions and information entropy have some graph-theoretical properties. Given a non-empty universe U, all the partitions G on U are taken as nodes, and a relation V between partitions are defined and taken as edges. The graph obtained is denoted by (G,V), which represents the connections between partitions on U. According to the values of the information entropy of partitions, a directed graph $(G,\overrightarrow{V})$ is defined on (G,V). It will be proved that there is a set of partitions with the minimal entropy; and a set of partitions with the maximal entropy; and the entropy is non-decreasing on any directed pathes in $(G,\overrightarrow{V})$ from a partition with the minimal entropy to one of the partitions with the maximal entropy. Hence, in $(G,\overrightarrow{V})$ the information entropy of partitions is represented in a clearly structured way.