Comparative Study of Classification of Split Bulk Grams Using Different Significant Feature Selection Techniques

2018 ◽  
Vol 9 (3) ◽  
pp. 225
Author(s):  
K.R. Pushpalatha ◽  
Asha Gowda Karegowda
Keyword(s):  
Feature Selection ◽  
Comparative Study ◽  
Significant Feature ◽  
Feature Selection Techniques

CLASSIFICATION OF HIGH-DIMENSIONAL MICROARRAY DATA WITH A TWO-STEP PROCEDURE VIA A WILCOXON CRITERION AND MULTILAYER PERCEPTRON

2011 ◽  
Vol 10 (01) ◽  
pp. 1-14
Author(s):  
VLADIMIR NIKULIN ◽  
TIAN-HSIANG HUANG ◽  
GEOFFREY J. MCLACHLAN
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
High Dimensional ◽  
Second Step ◽  
Support Vector ◽  
Step Procedure ◽  
Leave One Out ◽  
Natural Combination ◽  
Feature Selection Techniques

The method presented in this paper is novel as a natural combination of two mutually dependent steps. Feature selection is a key element (first step) in our classification system, which was employed during the 2010 International RSCTC data mining (bioinformatics) Challenge. The second step may be implemented using any suitable classifier such as linear regression, support vector machine or neural networks. We conducted leave-one-out (LOO) experiments with several feature selection techniques and classifiers. Based on the LOO evaluations, we decided to use feature selection with the separation type Wilcoxon-based criterion for all final submissions. The method presented in this paper was tested successfully during the RSCTC data mining Challenge, where we achieved the top score in the Basic track.

Hybrid Ensemble Learning Methods for Classification of Microarray Data

2020 ◽  
pp. 707-725
Author(s):  
Sujata Dash
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Microarray Data ◽  
Classification Model ◽  
Rotation Forest ◽  
Ensemble Technique ◽  
Basic Characteristics ◽  
Microarray Datasets ◽  
Feature Selection Techniques

Efficient classification and feature extraction techniques pave an effective way for diagnosing cancers from microarray datasets. It has been observed that the conventional classification techniques have major limitations in discriminating the genes accurately. However, such kind of problems can be addressed by an ensemble technique to a great extent. In this paper, a hybrid RotBagg ensemble framework has been proposed to address the problem specified above. This technique is an integration of Rotation Forest and Bagging ensemble which in turn preserves the basic characteristics of ensemble architecture i.e., diversity and accuracy. Three different feature selection techniques are employed to select subsets of genes to improve the effectiveness and generalization of the RotBagg ensemble. The efficiency is validated through five microarray datasets and also compared with the results of base learners. The experimental results show that the correlation based FRFR with PCA-based RotBagg ensemble form a highly efficient classification model.

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