A Study on Feature Subset Selection Using Rough Set Theory

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.

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A Diagnostic Feature Subset Selection of Breast Tumor Based on Neighborhood Rough Set Model

Journal of the Korea Industrial Information Systems Research ◽

10.9723/jksiis.2016.21.6.013 ◽

2016 ◽

Vol 21 (6) ◽

pp. 13-21

Author(s):

Chang-Sik Son ◽

Rock-Hyun Choi ◽

Won-Seok Kang ◽

Jong-Ha Lee

Keyword(s):

Rough Set ◽

Breast Tumor ◽

Subset Selection ◽

Feature Subset Selection ◽

Diagnostic Feature ◽

Feature Subset ◽

Neighborhood Rough Set ◽

Selection Of

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Rough Set-Based Feature Subset Selection Technique Using Jaccard’s Similarity Index

Advances in Intelligent Systems and Computing - Proceedings of International Ethical Hacking Conference 2018 ◽

10.1007/978-981-13-1544-2_39 ◽

2018 ◽

pp. 477-487

Author(s):

Bhawna Tibrewal ◽

Gargi Sur Chaudhury ◽

Sanjay Chakraborty ◽

Animesh Kairi

Keyword(s):

Rough Set ◽

Similarity Index ◽

Subset Selection ◽

Feature Subset Selection ◽

Feature Subset ◽

Selection Technique ◽

Jaccard's Similarity Index

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Feature Subset Selection of Semi-supervised Data: An Intuitionistic Fuzzy-Rough Set-Based Concept

Advances in Intelligent Systems and Computing - Proceedings of International Ethical Hacking Conference 2018 ◽

10.1007/978-981-13-1544-2_25 ◽

2018 ◽

pp. 303-315 ◽

Cited By ~ 1

Author(s):

Shivam Shreevastava ◽

Anoop Tiwari ◽

Tanmoy Som

Keyword(s):

Rough Set ◽

Subset Selection ◽

Feature Subset Selection ◽

Feature Subset ◽

Fuzzy Rough Set ◽

Intuitionistic Fuzzy ◽

Selection Of

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Feature subset selection approach based on fuzzy rough set for high-dimensional data

2014 IEEE International Conference on Granular Computing (GrC) ◽

10.1109/grc.2014.6982810 ◽

2014 ◽

Cited By ~ 4

Author(s):

Changyou Guo ◽

Xuefeng Zheng

Keyword(s):

Rough Set ◽

High Dimensional Data ◽

Subset Selection ◽

Feature Subset Selection ◽

High Dimensional ◽

Feature Subset ◽

Fuzzy Rough Set ◽

Selection Approach

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Data Reduction with Rough Sets

Encyclopedia of Data Warehousing and Mining, Second Edition ◽

10.4018/978-1-60566-010-3.ch087 ◽

2011 ◽

pp. 556-560 ◽

Cited By ~ 1

Author(s):

Richard Jensen

Keyword(s):

Feature Selection ◽

Knowledge Discovery ◽

Set Theory ◽

Rough Set ◽

Data Reduction ◽

Rough Sets ◽

Rough Set Theory ◽

Feature Subset ◽

Data Dependencies ◽

Additional Information

Data reduction is an important step in knowledge discovery from data. The high dimensionality of databases can be reduced using suitable techniques, depending on the requirements of the data mining processes. These techniques fall in to one of the following categories: those that transform the underlying meaning of the data features and those that are semantics-preserving. Feature selection (FS) methods belong to the latter category, where a smaller set of the original features is chosen based on a subset evaluation function. The process aims to determine a minimal feature subset from a problem domain while retaining a suitably high accuracy in representing the original features. In knowledge discovery, feature selection methods are particularly desirable as they facilitate the interpretability of the resulting knowledge. For this, rough set theory has been successfully used as a tool that enables the discovery of data dependencies and the reduction of the number of features contained in a dataset using the data alone, while requiring no additional information.

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A Fast Feature Selection Algorithm by Accelerating Computation of Fuzzy Rough Set-Based Information Entropy

Entropy ◽

10.3390/e20100788 ◽

2018 ◽

Vol 20 (10) ◽

pp. 788 ◽

Cited By ~ 4

Author(s):

Xiao Zhang ◽

Xia Liu ◽

Yanyan Yang

Keyword(s):

Feature Selection ◽

Set Theory ◽

Rough Set ◽

Information Entropy ◽

Fast Algorithm ◽

Rough Set Theory ◽

Computer Applications ◽

Feature Subset ◽

Fuzzy Rough Set ◽

Effective Measure

The information entropy developed by Shannon is an effective measure of uncertainty in data, and the rough set theory is a useful tool of computer applications to deal with vagueness and uncertainty data circumstances. At present, the information entropy has been extensively applied in the rough set theory, and different information entropy models have also been proposed in rough sets. In this paper, based on the existing feature selection method by using a fuzzy rough set-based information entropy, a corresponding fast algorithm is provided to achieve efficient implementation, in which the fuzzy rough set-based information entropy taking as the evaluation measure for selecting features is computed by an improved mechanism with lower complexity. The essence of the acceleration algorithm is to use iterative reduced instances to compute the lambda-conditional entropy. Numerical experiments are further conducted to show the performance of the proposed fast algorithm, and the results demonstrate that the algorithm acquires the same feature subset to its original counterpart, but with significantly less time.

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