An Attribute Reduction Method Based on Rough Set and SVM and with Application in Oil-Gas Prediction

2007 ◽  
Keyword(s):  
Rough Set ◽  
Reduction Method ◽  
Attribute Reduction ◽  
Oil Gas

Feature Based Rule Learner in Noisy Environment Using Neighbourhood Rough Set Model

2010 ◽  
Vol 2 (2) ◽  
pp. 66-85 ◽  
Author(s):  
Yang Liu ◽  
Luyang Jiao ◽  
Guohua Bai ◽  
Boqin Feng
Keyword(s):  
Rough Set ◽  
Real World ◽  
Reduction Method ◽  
Attribute Reduction ◽  
Noisy Environment ◽  
Feature Selection Problem ◽  
Real World Applications ◽  
Rule Sets ◽  
Feature Based ◽  
Real World Problems

From the perspective of cognitive informatics, cognition can be viewed as the acquisition of knowledge. In real-world applications, information systems usually contain some degree of noisy data. A new model proposed to deal with the hybrid-feature selection problem combines the neighbourhood approximation and variable precision rough set models. Then rule induction algorithm can learn from selected features in order to reduce the complexity of rule sets. Through proposed integration, the knowledge acquisition process becomes insensitive to the dimensionality of data with a pre-defined tolerance degree of noise and uncertainty for misclassification. When the authors apply the method to a Chinese diabetic diagnosis problem, the hybrid-attribute reduction method selected only five attributes from totally thirty-four measurements. Rule learner produced eight rules with average two attributes in the left part of an IF-THEN rule form, which is a manageable set of rules. The demonstrated experiment shows that the present approach is effective in handling real-world problems.

A Parallel Computation Method for Heuristic Attribute Reduction Using Reduced Decision Tables

2013 ◽  
Vol 17 (3) ◽  
pp. 371-376 ◽  
Author(s):  
Yasuo Kudo ◽  
◽  
Tetsuya Murai ◽  
Keyword(s):  
Data Mining ◽  
Parallel Computation ◽  
Set Theory ◽  
Rough Set ◽  
Reduction Method ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Computation Method ◽  
Decision Tables

In this paper, we propose a parallel computation framework for a heuristic attribute reduction method. Attribute reduction is a key technique to use rough set theory as a tool in data mining. The authors have previously proposed a heuristic attribute reduction method to compute as many relative reducts as possible from a given dataset with numerous attributes. We parallelize our method by using open multiprocessing. We also evaluate the performance of a parallelized attribute reduction method by experiments.

Binary Resolution Matrix Based Incremental Attribute Reduction for Fault Diagnosis

2016 ◽  
Vol 693 ◽  
pp. 1346-1349
Author(s):  
Xiao Yu Chen ◽  
Wen Liao Du ◽  
An Sheng Li ◽  
Kun Li ◽  
Chun Hua Qian
Keyword(s):  
Fault Diagnosis ◽  
Set Theory ◽  
Rough Set ◽  
Reduction Method ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Rotating Machinery ◽  
Reduction Algorithm

Rough set theory is a useful tool for attribute reduction of fault diagnosis for rotating machinery, but cannot be efficiently used to sample increased areas. Aiming at the problem of incremental attribute reduction, a novel attribute reduction algorithm was put forward based on the binary resolution matrix for the two updating situations and the algorithm had a low space complex. Finally, with the fault diagnosis experiments of the bearing, the attribute reduction method was proved to be correct.

The fitness-rough: A new attribute reduction method based on statistical and rough set theory

2008 ◽  
Vol 12 (1) ◽  
pp. 73-87 ◽  
Author(s):  
Yun-Huoy Choo ◽  
Azuraliza Abu Bakar ◽  
Abdul Razak Hamdan
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Reduction Method ◽  
Rough Set Theory ◽  
Attribute Reduction

A New Heuristic Attribute Reduction Method Based on Boolean Matrix

2011 ◽  
Vol 50-51 ◽  
pp. 605-609
Author(s):  
Yan Hong Xie
Keyword(s):  
Rough Set ◽  
Reduction Method ◽  
Attribute Reduction ◽  
Research Topic ◽  
Boolean Matrix ◽  
Important Research ◽  
Important Research Topic

Attribute reduction of rough set is a very important research topic. The shortcoming of Reference [8,9]’s method is got a superset of a true reduction sometimes, and the disadvantage of Reference [10]’s algorithm is could not get a right attribute reduction sometimes. To overcome the above shortcomings, a new heuristic attribute reduction method based on Boolean matrix is put forward. Finally, the method’s feasibility and validity are proved by examples.

Attribute Reduction Method of Uncertain Information

2010 ◽  
Vol 108-111 ◽  
pp. 568-573
Author(s):  
E Xu ◽  
Liang Shan Shao ◽  
Zhu Qiao ◽  
Guang Hui Cao ◽  
Feng Qiu
Keyword(s):  
Rough Set ◽  
Reduction Method ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Uncertain Information ◽  
Heuristic Knowledge ◽  
The Core ◽  
Reduction Experiment ◽  
Speed Up ◽  
Incomplete Information Systems

To attribute reduction in an uncertain information system, this paper proposed a method of attribute reduction based on rough set theory. This reduction method gives the concepts of tolerance relationship, attribute significance and tolerance relationship similar matrix to deal with the inconsistency problem of in the information table. And then obtains the core attributes of incomplete information systems via the tolerance relationship similar matrix. Finally, according to attribute frequency in the tolerance relationship similar matrix, as the heuristic knowledge, makes use of binsearch heuristic algorithm to calculate the candidate attribute expansion so that it can reduce the expansion times to speed up reduction. Experiment results show that the algorithm is simple and effective.

scholarly journals Research on Feature Selection/Attribute Reduction Method Based on Rough Set Theory

2019 ◽  
Vol 154 ◽  
pp. 194-198
Author(s):  
Shi Qiang Wang ◽  
Cai Yun Gao ◽  
Chang Luo ◽  
Gui Mei Zheng ◽  
Yan Nian Zhou
Keyword(s):  
Feature Selection ◽  
Set Theory ◽  
Rough Set ◽  
Reduction Method ◽  
Rough Set Theory ◽  
Attribute Reduction
Sign in / Sign up

Export Citation Format

Share Document