A Hybrid Improved Ant Colony Optimization and Random Forests Feature Selection Method for Microarray Data

2009 ◽  
Author(s):  
Wen Xiong ◽  
Cong Wang
Keyword(s):  
Feature Selection ◽  
Ant Colony Optimization ◽  
Random Forests ◽  
Microarray Data ◽  
Feature Selection Method ◽  
Selection Method ◽  
Ant Colony

scholarly journals Ant Colony Optimization Based Feature Selection Method for QEEG Data Classification

2014 ◽  
Vol 11 (3) ◽  
pp. 243 ◽  
Author(s):  
Turker Tekin Erguzel ◽  
Serhat Ozekes ◽  
Selahattin Gultekin ◽  
Nevzat Tarhan
Keyword(s):  
Feature Selection ◽  
Ant Colony Optimization ◽  
Feature Selection Method ◽  
Data Classification ◽  
Selection Method ◽  
Ant Colony

A fuzzy gaussian rank aggregation ensemble feature selection method for microarray data

2021 ◽  
Vol 24 (4) ◽  
pp. 289-301
Author(s):  
B. Venkatesh ◽  
J. Anuradha
Keyword(s):  
Feature Selection ◽  
Microarray Data ◽  
Classification Accuracy ◽  
Performance Metrics ◽  
Feature Selection Method ◽  
Selection Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Binary Particle Swarm Optimization ◽  
Selection Methods

In Microarray Data, it is complicated to achieve more classification accuracy due to the presence of high dimensions, irrelevant and noisy data. And also It had more gene expression data and fewer samples. To increase the classification accuracy and the processing speed of the model, an optimal number of features need to extract, this can be achieved by applying the feature selection method. In this paper, we propose a hybrid ensemble feature selection method. The proposed method has two phases, filter and wrapper phase in filter phase ensemble technique is used for aggregating the feature ranks of the Relief, minimum redundancy Maximum Relevance (mRMR), and Feature Correlation (FC) filter feature selection methods. This paper uses the Fuzzy Gaussian membership function ordering for aggregating the ranks. In wrapper phase, Improved Binary Particle Swarm Optimization (IBPSO) is used for selecting the optimal features, and the RBF Kernel-based Support Vector Machine (SVM) classifier is used as an evaluator. The performance of the proposed model are compared with state of art feature selection methods using five benchmark datasets. For evaluation various performance metrics such as Accuracy, Recall, Precision, and F1-Score are used. Furthermore, the experimental results show that the performance of the proposed method outperforms the other feature selection methods.

scholarly journals Pathway-Based Feature Selection Algorithm for Cancer Microarray Data

2009 ◽  
Vol 2009 ◽  
pp. 1-16 ◽  
Author(s):  
Nirmalya Bandyopadhyay ◽  
Tamer Kahveci ◽  
Steve Goodison ◽  
Y. Sun ◽  
Sanjay Ranka
Keyword(s):  
Feature Selection ◽  
Microarray Data ◽  
Feature Selection Method ◽  
Gene Signature ◽  
Selection Method ◽  
Test Accuracy ◽  
Clinical Markers ◽  
Gene Expressions ◽  
Regulatory Pathways ◽  
New Feature

Classification of cancers based on gene expressions produces better accuracy when compared to that of the clinical markers. Feature selection improves the accuracy of these classification algorithms by reducing the chance of overfitting that happens due to large number of features. We develop a new feature selection method called Biological Pathway-based Feature Selection (BPFS) for microarray data. Unlike most of the existing methods, our method integrates signaling and gene regulatory pathways with gene expression data to minimize the chance of overfitting of the method and to improve the test accuracy. Thus, BPFS selects a biologically meaningful feature set that is minimally redundant. Our experiments on published breast cancer datasets demonstrate that all of the top 20 genes found by our method are associated with cancer. Furthermore, the classification accuracy of our signature is up to 18% better than that of vant Veers 70 gene signature, and it is up to 8% better accuracy than the best published feature selection method, I-RELIEF.

MMBDE: A Two-stage Hybrid Feature Selection Method From Microarray Data

2021 ◽  
Author(s):  
Weidong Xie ◽  
Yuhuan Chi ◽  
Linjie Wang ◽  
Kun Yu ◽  
Wei Li
Keyword(s):  
Feature Selection ◽  
Microarray Data ◽  
Feature Selection Method ◽  
Selection Method ◽  
Two Stage
Sign in / Sign up

Export Citation Format

Share Document