Feature selection in independent component subspace for microarray data classification

2006 ◽  
Vol 69 (16-18) ◽  
pp. 2407-2410 ◽  
Author(s):  
Chun-Hou Zheng ◽  
De-Shuang Huang ◽  
Li Shang
Keyword(s):  
Feature Selection ◽  
Microarray Data ◽  
Data Classification ◽  
Independent Component

scholarly journals Comparative analysis of ReliefF-SVM and CFS-SVM for microarray data classification

2021 ◽  
Vol 11 (4) ◽  
pp. 3393
Author(s):  
Mochamad Agusta Naofal Hakim ◽  
Adiwijaya Adiwijaya ◽  
Widi Astuti
Keyword(s):  
Feature Selection ◽  
Microarray Data ◽  
Causes Of Death ◽  
Data Classification ◽  
World Health ◽  
Support Vector ◽  
Feature Selection Technique ◽  
Cancer Disease ◽  
Cancer Symptoms ◽  
The World

Cancer is one of the main causes of death in the world where the World Health Organization (WHO) recognized cancer as among the top causes of death in 2018. Thus, detecting cancer symptoms is paramount in order to cure and subsequently reduce the casualties due to cancer disease. Many studies have been developed data mining approaches to detect symptoms of cancer through a classifying human gene data expression. One popular approach is using microarray data based on DNA. However, DNA microarray data has many dimensions that can have a detrimental effect on the accuracy of classification. Therefore, before performing classification, a feature selection technique must be used to eliminate features that do not have important information to support the classification process. The feature selection techniques used were ReliefF and correlation-based feature selection (CFS) and a classification technique used in this study is support vector machine (SVM). Several testing schemes were applied in this analysis to compare the performance of ReliefF and CFS with SVM. It showed that the ReliefF outperformed compared with CFS as microarray data classification approach.

Microarray Data Classification Based on Evolutionary Multiple Classifier System

2011 ◽  
Vol 130-134 ◽  
pp. 2077-2080
Author(s):  
Zheng Gang Gu ◽  
Kun Hong Liu
Keyword(s):  
Feature Selection ◽  
Microarray Data ◽  
Selection Process ◽  
Data Classification ◽  
Research Area ◽  
Ensemble Method ◽  
Multiple Classifier System ◽  
Classifier System ◽  
Classifier Selection ◽  
Multiple Classifier

Designing an evolutionary multiple classifier system (MCS) is a relatively new research area. In this paper, we propose a genetic algorithm (GA) based MCS for microarray data classification. We construct a feature poll with different feature selection methods first, and then a multi-objective GA is applied to implement ensemble feature selection process so as to generate a set of classifiers. When this GA stops, a set of base classifiers are generated. Here we use all the nondominated individuals in last generation to build an ensemble system and test the proposed ensemble method and the method that apply a classifier selection process to select proper classifiers from all the individuals in last generation. The experimental results show the proposed ensemble method is roubust and can lead to promising results.

Distributed feature selection: An application to microarray data classification

2015 ◽  
Vol 30 ◽  
pp. 136-150 ◽  
Author(s):  
V. Bolón-Canedo ◽  
N. Sánchez-Maroño ◽  
A. Alonso-Betanzos
Keyword(s):  
Feature Selection ◽  
Microarray Data ◽  
Data Classification

Microarray data classification based on ensemble independent component selection

2009 ◽  
Vol 39 (11) ◽  
pp. 953-960 ◽  
Author(s):  
Kun-Hong Liu ◽  
Bo Li ◽  
Qing-Qiang Wu ◽  
Jun Zhang ◽  
Ji-Xiang Du ◽  
...  
Keyword(s):  
Microarray Data ◽  
Data Classification ◽  
Independent Component ◽  
Component Selection
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