Fuzzy Discretization and Rough Set based Feature Selection for High-Dimensional Classification
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@Article{JICS-13-168,
author = {Prema Ramasamy and Premalatha Kandhasamy},
title = {Fuzzy Discretization and Rough Set based Feature Selection for High-Dimensional Classification},
journal = {Journal of Information and Computing Science},
year = {2024},
volume = {13},
number = {3},
pages = {168--178},
abstract = {1 Prema Ramasamy, Assistant Professor, New Horizon College of Engineering, Bangalore
E-mail:premabit@gmail.com
2 Professor, Department of Computer Science and Engineering, Bannari Amman Institute of Techlology,
Sathyamangalam.
(Received May 11 2018, accepted July 16 2018)
Contemporary biological technologies like gene expression microarrays produce extremely high-
dimensional datasets with limited samples. Analysis of gene expression data is essential in microarray gene expression
studies in order to retrieve the required information. Gene expression data generally contain a large number of genes
but a small number of samples. The complicated relations among the different genes make analysis more difficult,
and removing irrelevant genes improves the quality of results. In this regard, a new feature selection algorithm called
2-level MRMS is presented based on rough set theory. It selects a set of genes from microarray data by maximizing
the relevance and significance of the selected genes. The paper also presents a novel discretization method, Gaussian
Fuzzy Discretization based on fuzzy logic to discretize the continuous gene expression values. The performance of
the proposed algorithm, along with a comparison with other related feature selection methods, is studied using the
classification accuracy of k-Nearest Neighbor (kNN) and Support Vector Machine (SVM) on four microarray data
sets. The experimental results show that the genes selected using 2-level MRMS feature selection give high
classification accuracy than other methods.
},
issn = {1746-7659},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/jics/22442.html}
}
TY - JOUR
T1 - Fuzzy Discretization and Rough Set based Feature Selection for High-Dimensional Classification
AU - Prema Ramasamy and Premalatha Kandhasamy
JO - Journal of Information and Computing Science
VL - 3
SP - 168
EP - 178
PY - 2024
DA - 2024/01
SN - 13
DO - http://doi.org/
UR - https://global-sci.org/intro/article_detail/jics/22442.html
KW - classification, feature selection, fuzzy discretization, high- dimensional data, maximum relevance and
maximum significance, microarray data
AB - 1 Prema Ramasamy, Assistant Professor, New Horizon College of Engineering, Bangalore
E-mail:premabit@gmail.com
2 Professor, Department of Computer Science and Engineering, Bannari Amman Institute of Techlology,
Sathyamangalam.
(Received May 11 2018, accepted July 16 2018)
Contemporary biological technologies like gene expression microarrays produce extremely high-
dimensional datasets with limited samples. Analysis of gene expression data is essential in microarray gene expression
studies in order to retrieve the required information. Gene expression data generally contain a large number of genes
but a small number of samples. The complicated relations among the different genes make analysis more difficult,
and removing irrelevant genes improves the quality of results. In this regard, a new feature selection algorithm called
2-level MRMS is presented based on rough set theory. It selects a set of genes from microarray data by maximizing
the relevance and significance of the selected genes. The paper also presents a novel discretization method, Gaussian
Fuzzy Discretization based on fuzzy logic to discretize the continuous gene expression values. The performance of
the proposed algorithm, along with a comparison with other related feature selection methods, is studied using the
classification accuracy of k-Nearest Neighbor (kNN) and Support Vector Machine (SVM) on four microarray data
sets. The experimental results show that the genes selected using 2-level MRMS feature selection give high
classification accuracy than other methods.
Prema Ramasamy and Premalatha Kandhasamy. (2024). Fuzzy Discretization and Rough Set based Feature Selection for High-Dimensional Classification.
Journal of Information and Computing Science. 13 (3).
168-178.
doi:
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