Rule Induction Based on Rough Sets from Possibilistic Data Tables

How to induce rules from data tables containing possibilistic information is described under using rough sets based on possible world semantics. A piece of possibilistic information is expressed in a normal and discrete possibility distribution. Under a degree of possibility, the incomplete data tab...

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Bibliographic Details
Published inIntegrated Uncertainty in Knowledge Modelling and Decision Making pp. 86 - 97
Main Authors Nakata, Michinori, Sakai, Hiroshi
Format Book Chapter
LanguageEnglish
Published Cham Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Incomplete information
Non-deterministic information
Possibilistic information
Rough sets
Rule induction
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Summary:How to induce rules from data tables containing possibilistic information is described under using rough sets based on possible world semantics. A piece of possibilistic information is expressed in a normal and discrete possibility distribution. Under a degree of possibility, the incomplete data table is derived from a possibilistic data table. Rough sets and rules are derived from incomplete data tables. The rough sets and rules obtained under every degree of possibility are aggregated from the viewpoints of certainty and possibility. As a result, rough sets consist of objects with a degree expressed in an interval and an object also supports rules with a degree expressed in an interval value. Furthermore, a criterion is introduced to judge whether or not an object is regarded as validly supporting rules.
ISBN:3030148149
9783030148140
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-030-14815-7_8