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.