Double-Granule Conditional-Entropies Based on Three-Level Granular Structures
| Autor: | Zhiwen Mo, Taopin Mu, Xianyong Zhang |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
granular computing Computer science General Physics and Astronomy lcsh:Astrophysics 02 engineering and technology Information theory Article conditional entropy Granulation 020901 industrial engineering & automation lcsh:QB460-466 0202 electrical engineering electronic engineering information engineering Entropy (information theory) lcsh:Science uncertainty Computer Science::Databases Computer Science::Information Theory rough set theory information theory three-level granular structures Conditional entropy Computer Science::Information Retrieval Granular computing Information processing lcsh:QC1-999 lcsh:Q 020201 artificial intelligence & image processing Rough set Decision table Algorithm lcsh:Physics |
| Zdroj: | Entropy Entropy, Vol 21, Iss 7, p 657 (2019) Volume 21 Issue 7 |
| ISSN: | 1099-4300 |
| Popis: | Rough set theory is an important approach for data mining, and it refers to Shannon&rsquo s information measures for uncertainty measurements. The existing local conditional-entropies have both the second-order feature and application limitation. By improvements of hierarchical granulation, this paper establishes double-granule conditional-entropies based on three-level granular structures (i.e., micro-bottom, meso-middle, macro-top ), and then investigates the relevant properties. In terms of the decision table and its decision classification, double-granule conditional-entropies are proposed at micro-bottom by the dual condition-granule system. By virtue of successive granular summation integrations, they hierarchically evolve to meso-middle and macro-top, to respectively have part and complete condition-granulations. Then, the new measures acquire their number distribution, calculation algorithm, three bounds, and granulation non-monotonicity at three corresponding levels. Finally, the hierarchical constructions and achieved properties are effectively verified by decision table examples and data set experiments. Double-granule conditional-entropies carry the second-order characteristic and hierarchical granulation to deepen both the classical entropy system and local conditional-entropies, and thus they become novel uncertainty measures for information processing and knowledge reasoning. |
| Databáze: | OpenAIRE |
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