Fast Algorithm of Attribute Reduction Based on the Complementation of Boolean Function

2014 ◽  
pp. 25-41 ◽  
Author(s):  
Grzegorz Borowik ◽  
Tadeusz Łuba
Keyword(s):  
Boolean Function ◽  
Fast Algorithm ◽  
Attribute Reduction

Attribute Reduction Using Bayesian Decision Theoretic Rough Set Models

2014 ◽  
Vol 1 (1) ◽  
pp. 15-31 ◽  
Author(s):  
Sharmistha Bhattacharya Halder ◽  
Kalyani Debnath
Keyword(s):  
Boolean Function ◽  
Rough Set ◽  
Bayesian Method ◽  
Probabilistic Approach ◽  
Attribute Reduction ◽  
Bayesian Decision ◽  
Discernibility Matrix ◽  
Variable Precision ◽  
Precision Method ◽  
Positive Boundary

Bayesian Decision theoretic rough set has been invented by the author. In this paper the attribute reduction by the aid of Bayesian decision theoretic rough set has been studied. Lot of other methods are there for attribute reduction such as Variable precision method, probabilistic approach, Bayesian method, Pawlaks rough set method using Boolean function. But with the help of some example it is shown that Bayesian decision theoretic rough set model gives better result than other method. Lastly an example of HIV /AIDS is taken and attribute reduction is done by this new method and various other method. It is shown that this method gives better result than the previously defined methods. By this method the authors get only the reduced attribute age which is the best significant attribute. Though in Pawlak model age sex or age living status are the reduced attribute and variable precision method fails to work here. In this paper attribute reduction is done by the help of discernibility matrix after determining the positive, boundary and negative region. This model is a hybrid model of Bayesian rough set model and decision theory. So this technique gives better result than Bayesian method and decision theoretic rough set method.

ATTRIBUTE REDUCTION IN VARIABLE PRECISION ROUGH SET MODEL

2006 ◽  
Vol 14 (04) ◽  
pp. 461-479 ◽  
Author(s):  
MASAHIRO INUIGUCHI
Keyword(s):  
Boolean Function ◽  
Rough Set ◽  
Rough Sets ◽  
Attribute Reduction ◽  
Variable Precision ◽  
Variable Precision Rough Set ◽  
Variable Precision Rough Sets

In this paper, attribute reduction in variable precision rough set model is discussed. Several kinds of reducts preserving some of lower approximations, upper approximations, boundary regions and the unpredictable region are discussed. Relations among those kinds of reducts are investigated. As a basis for reduct computation, Boolean function representations of the preservation of lower approximations, upper approximations, boundary regions and the unpredictable region are discussed. Throughout this paper, the great difference between the analysis using variable precision rough sets and the classical rough set analysis is emphasized.

Optimization of Perfect Reconstruction Cosine-Modulated Filter Banks: Fast Algorithm

2001 ◽  
Vol 56 (12) ◽  
pp. 8 ◽  
Author(s):  
Oscar G. Ibarra-Manzano ◽  
Yuriy V. Shkvarko ◽  
Rene Jaime-Rivas ◽  
Jose A. Andrade-Lucio ◽  
Gordana Jovanovic-Dolecek
Keyword(s):  
Fast Algorithm ◽  
Filter Banks ◽  
Perfect Reconstruction

Application of an attribute reduction algorithm based on discernibility matrix in PBNM

2014 ◽  
Author(s):  
Shuo Feng ◽  
Haiying Chu ◽  
Xuyang Wang ◽  
Yuanka Liang ◽  
Xianwei Shi ◽  
...  
Keyword(s):  
Attribute Reduction ◽  
Reduction Algorithm ◽  
Discernibility Matrix
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