Applying particle swarm optimization-based decision tree classifier for cancer classification on gene expression data

2014 ◽  
Vol 24 ◽  
pp. 773-780 ◽  
Author(s):  
Kun-Huang Chen ◽  
Kung-Jeng Wang ◽  
Kung-Min Wang ◽  
Melani-Adrian Angelia
Keyword(s):  
Gene Expression ◽  
Particle Swarm Optimization ◽  
Decision Tree ◽  
Gene Expression Data ◽  
Particle Swarm ◽  
Cancer Classification ◽  
Expression Data ◽  
Decision Tree Classifier ◽  
Swarm Optimization ◽  
Tree Classifier

Analysis of Micro Array Gene Expression Data Using Multi-Objective Binary Particle Swarm Optimization with Fuzzy Weighted Clustering (MOBPSO-FWC) Technique

2020 ◽  
Vol 10 (5) ◽  
pp. 1049-1056
Author(s):  
M. Kalaiarasu ◽  
J. Anitha
Keyword(s):  
Gene Expression ◽  
Particle Swarm Optimization ◽  
Gene Expression Data ◽  
Expression Profiles ◽  
Particle Swarm ◽  
Binary Particle Swarm Optimization ◽  
Expression Data ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Weighted Clustering

In the rapidly advancing field of genomics, microarray technologies have turned into a ground-breaking system on simultaneous monitoring the expression patterns of multiple genes under various arrangements of constraints. A fundamental errand is to propose diagnostic techniques to distinguish cluster of genes comparative expression designs and are initiated by comparative conditions. And furthermore, the relating investigation has issue is to cluster multi-condition gene expression data. To overcome these issues, the vast measure of data obtained by this technology, resort to clustering methods that distinguish clusters of genes of share similar expression profiles. The motivation of this work is to introduce a clustering method in microarray gene expression data analysis. Multi-Objective Binary Particle Swarm Optimization with Fuzzy Weighted Clustering (MOBPSOFWC) algorithm is proposed to analyze gene expression data. In high dimensionality, a quick heuristic based pre-processing technique is employed to diminish some of the basic domain features from the initial feature set. Since these pre-processed and reduced features are still high dimensional, the proposed MOBPSO algorithm is implemented in MATLAB tool used for finding further feature subsets. The investigative are directed to distinguish the execution of the proposed work with existing clustering approaches.

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