An exact feature selection algorithm based on rough set theory

Complexity ◽  
2014 ◽  
Vol 20 (5) ◽  
pp. 50-62 ◽  
Author(s):  
Mohammad Taghi Rezvan ◽  
Ali Zeinal Hamadani ◽  
Seyed Reza Hejazi
Keyword(s):  
Feature Selection ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Selection Algorithm ◽  
Feature Selection Algorithm

scholarly journals Feature Selection Combining Information Theory View and Algebraic View in the Neighborhood Decision System

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 704
Author(s):  
Jiucheng Xu ◽  
Kanglin Qu ◽  
Meng Yuan ◽  
Jie Yang
Keyword(s):  
Information Theory ◽  
Feature Selection ◽  
Rough Set ◽  
Rough Set Theory ◽  
Classification Performance ◽  
Joint Entropy ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Selection Algorithms ◽  
Theory View

Feature selection is one of the core contents of rough set theory and application. Since the reduction ability and classification performance of many feature selection algorithms based on rough set theory and its extensions are not ideal, this paper proposes a feature selection algorithm that combines the information theory view and algebraic view in the neighborhood decision system. First, the neighborhood relationship in the neighborhood rough set model is used to retain the classification information of continuous data, to study some uncertainty measures of neighborhood information entropy. Second, to fully reflect the decision ability and classification performance of the neighborhood system, the neighborhood credibility and neighborhood coverage are defined and introduced into the neighborhood joint entropy. Third, a feature selection algorithm based on neighborhood joint entropy is designed, which improves the disadvantage that most feature selection algorithms only consider information theory definition or algebraic definition. Finally, experiments and statistical analyses on nine data sets prove that the algorithm can effectively select the optimal feature subset, and the selection result can maintain or improve the classification performance of the data set.

Online early terminated streaming feature selection based on Rough Set theory

2021 ◽  
pp. 107993
Author(s):  
Peng Zhou ◽  
Peipei Li ◽  
Shu Zhao ◽  
Yanping Zhang
Keyword(s):  
Feature Selection ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory

Intuitionistic Fuzzy Neighborhood Rough Set Model for Feature Selection

2018 ◽  
Vol 7 (2) ◽  
pp. 75-84 ◽  
Author(s):  
Shivam Shreevastava ◽  
Anoop Kumar Tiwari ◽  
Tanmoy Som
Keyword(s):  
Feature Selection ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Continuous Data ◽  
Feature Subset ◽  
Data Set ◽  
Intuitionistic Fuzzy ◽  
Neighborhood Models ◽  
Neighborhood Rough Set

Feature selection is one of the widely used pre-processing techniques to deal with large data sets. In this context, rough set theory has been successfully implemented for feature selection of discrete data set but in case of continuous data set it requires discretization, which may cause information loss. Fuzzy rough set theory approaches have also been used successfully to resolve this issue as it can handle continuous data directly. Moreover, almost all feature selection techniques are used to handle homogeneous data set. In this article, the center of attraction is on heterogeneous feature subset reduction. A novel intuitionistic fuzzy neighborhood models have been proposed by combining intuitionistic fuzzy sets and neighborhood rough set models by taking an appropriate pair of lower and upper approximations and generalize it for feature selection, supported with theory and its validation. An appropriate algorithm along with application to a data set has been added.

scholarly journals Rough set and teaching learning based optimization technique for optimal features selection

2013 ◽  
Vol 3 (1) ◽  
Author(s):  
Suresh Satapathy ◽  
Anima Naik ◽  
K. Parvathi
Keyword(s):  
Feature Selection ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Optimization Technique ◽  
Feature Selection Method ◽  
Optimization Techniques ◽  
Features Selection ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

AbstractRough set theory has been one of the most successful methods used for feature selection. However, this method is still not able to find optimal subsets. But it can be made to be optimal using different optimization techniques. This paper proposes a new feature selection method based on Rough Set theory with Teaching learning based optimization (TLBO). The proposed method is experimentally compared with other hybrid Rough Set methods such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE) and the empirical results reveal that the proposed approach could be used for feature selection as this performs better in terms of finding optimal features and doing so in quick time.

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