Knowledge Reduction and its Rough Entropy Representation of Decision Tables in Rough Set

2007 ◽  
Author(s):  
Jiucheng Xu ◽  
Lin Sun
Keyword(s):  
Rough Set ◽  
Knowledge Reduction ◽  
Decision Tables

scholarly journals A Variable Precision Attribute Reduction Approach in Multilabel Decision Tables

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Hua Li ◽  
Deyu Li ◽  
Yanhui Zhai ◽  
Suge Wang ◽  
Jing Zhang
Keyword(s):  
Feature Selection ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Mathematical Tool ◽  
Knowledge Reduction ◽  
Variable Precision ◽  
Multilabel Learning ◽  
Decision Tables

Owing to the high dimensionality of multilabel data, feature selection in multilabel learning will be necessary in order to reduce the redundant features and improve the performance of multilabel classification. Rough set theory, as a valid mathematical tool for data analysis, has been widely applied to feature selection (also called attribute reduction). In this study, we propose a variable precision attribute reduct for multilabel data based on rough set theory, calledδ-confidence reduct, which can correctly capture the uncertainty implied among labels. Furthermore, judgement theory and discernibility matrix associated withδ-confidence reduct are also introduced, from which we can obtain the approach to knowledge reduction in multilabel decision tables.

Knowledge Reduction in Inconsistent Decision Tables

2006 ◽  
pp. 626-635 ◽  
Author(s):  
Qihe Liu ◽  
Leiting Chen ◽  
Jianzhong Zhang ◽  
Fan Min
Keyword(s):  
Knowledge Reduction ◽  
Decision Tables

Rough Set Classification Algorithms Based on Texture Feature

2013 ◽  
Vol 347-350 ◽  
pp. 3119-3122
Author(s):  
Yan Xue Dong ◽  
Fu Hai Huang
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Texture Classification ◽  
Texture Feature ◽  
Decision Table ◽  
Classification Algorithms ◽  
Reduction Algorithm ◽  
Discrete Method ◽  
Knowledge Reduction

The basic theory of rough set is given and a method for texture classification is proposed. According to the GCLM theory, texture feature is extracted and generate 32 feature vectors to form a decision table, find a minimum set of rules for classification after attribute discretization and knowledge reduction, experimental results show that using rough set theory in texture classification, accompanied by appropriate discrete method and reduction algorithm can get better classification results

Missing Data Estimation Using Rough Sets

2010 ◽  
pp. 94-116
Author(s):  
Tshilidzi Marwala
Keyword(s):  
Missing Data ◽  
Rough Set ◽  
Rough Sets ◽  
Real Data ◽  
Valuable Insight ◽  
Lower And Upper Approximations ◽  
Decision Tables ◽  
Missing Data Estimation ◽  
Data Estimation ◽  
Insight Into

A number of techniques for handling missing data have been presented and implemented. Most of these proposed techniques are unnecessarily complex and, therefore, difficult to use. This chapter investigates a hot-deck data imputation method, based on rough set computations. In this chapter, characteristic relations are introduced that describe incompletely specified decision tables and then these are used for missing data estimation. It has been shown that the basic rough set idea of lower and upper approximations for incompletely specified decision tables may be defined in a variety of different ways. Empirical results obtained using real data are given and they provide a valuable insight into the problem of missing data. Missing data are predicted with an accuracy of up to 99%.

THE ALGORITHM ON KNOWLEDGE REDUCTION IN INCOMPLETE INFORMATION SYSTEMS

2002 ◽  
Vol 10 (01) ◽  
pp. 95-103 ◽  
Author(s):  
JIYE LIANG ◽  
ZONGBEN XU
Keyword(s):  
Information System ◽  
Information Systems ◽  
Incomplete Information ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Np Hard ◽  
Knowledge Reduction ◽  
Np Hard Problem ◽  
Incomplete Information Systems

Rough set theory is emerging as a powerful tool for reasoning about data, knowledge reduction is one of the important topics in the research on rough set theory. It has been proven that finding the minimal reduct of an information system is a NP-hard problem, so is finding the minimal reduct of an incomplete information system. Main reason of causing NP-hard is combination problem of attributes. In this paper, knowledge reduction is defined from the view of information, a heuristic algorithm based on rough entropy for knowledge reduction is proposed in incomplete information systems, the time complexity of this algorithm is O(|A|2|U|). An illustrative example is provided that shows the application potential of the algorithm.

CLUSTERING OF DECISION TABLES TOWARD ROUGH SET-BASED GROUP DECISION AID

2011 ◽  
Vol 19 (supp01) ◽  
pp. 17-32 ◽  
Author(s):  
MASAHIRO INUIGUCHI ◽  
RYUTA ENOMOTO
Keyword(s):  
Hierarchical Clustering ◽  
Rough Set ◽  
Real World ◽  
Decision Aid ◽  
Group Decision ◽  
Decision Rules ◽  
Agglomerative Hierarchical Clustering ◽  
Clustering Techniques ◽  
Fuzzy C Means ◽  
Decision Tables

In order to analyze the distribution of individual opinions (decision rules) in a group, clustering of decision tables is proposed. An agglomerative hierarchical clustering (AHC) of decision tables has been examined. The result of AHC does not always optimize some criterion. We develop non-hierarchical clustering techniques for decision tables. In order to treat positive and negative evaluations to a common profile, we use a vector of rough membership values to represent individual opinion to a profile. Using rough membership values, we develop a K -means method as well as fuzzy c-means methods for clustering decision tables. We examined the proposed methods in clustering real world decision tables obtained by a questionnaire investigation.

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