Improved heterogeneous data fusion and multi‐scale feature selection method for lung cancer subtype classification

2021 ◽  
Author(s):  
Yanan Zhang ◽  
Juanjuan Zhao ◽  
Yan Qiang ◽  
Xiaotang Yang ◽  
Wei Wu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Feature Selection ◽  
Data Fusion ◽  
Feature Selection Method ◽  
Heterogeneous Data ◽  
Selection Method ◽  
Scale Feature ◽  
Multi Scale ◽  
Subtype Classification ◽  
Cancer Subtype

scholarly journals A Multi-Scale Feature Selection Method for Steganalytic Feature GFR

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 55063-55075 ◽  
Author(s):  
Xinquan Yu ◽  
Yuanyuan Ma ◽  
Ruixia Jin ◽  
Lige Xu ◽  
Xintao Duan
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Scale Feature ◽  
Multi Scale

scholarly journals Predicting Lung Cancer Patients’ Survival Time via Logistic Regression-based Models in a Quantitative Radiomic Framework

2019 ◽  
Author(s):  
S P Shayesteh ◽  
I Shiri ◽  
A H Karami ◽  
R Hashemian ◽  
S Kooranifar ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Logistic Regression ◽  
Feature Selection ◽  
Mutual Information ◽  
Cancer Patients ◽  
Feature Selection Method ◽  
Selection Method ◽  
Classification Models ◽  
Lung Cancer Patients ◽  
Pre Treatment

Objectives: The aim of this study was to predict the survival time of lung cancer patients using the advantages of both radiomics and logistic regression-based classification models.Material and Methods: Fifty-nine patients with primary lung adenocarcinoma were included in this retrospective study and pre-treatment contrast-enhanced CT images were acquired. The patients lived more than 2 years were classified as the ‘Alive’ class and otherwise as the ‘Dead’ class. In our proposed quantitative radiomic framework, we first extracted the associated regions of each lung lesion from pre-treatment CT images for each patient via grow cut segmentation algorithm. Then, 40 radiomic features were extracted from the segmented lung lesions. In order to enhance the generalizability of the classification models, the mutual information-based feature selection method was applied to each feature vector. We investigated the performance of six logistic regression-based classification models with consider to acceptable evaluation measures such as F1 score and accuracy.Results: It was observed that the mutual information feature selection method can help the classifier to achieve better predictive results. In our study, the Logistic regression (LR) and Dual Coordinate Descent method for Logistic Regression (DCD-LR) models achieved the best results indicating that these classification models have strong potential for classifying the more important class (i.e., the ‘Alive’ class).Conclusion: The proposed quantitative radiomic framework yielded promising results, which can guide physicians to make better and more precise decisions and increase the chance of treatment success.

A CBR framework with gradient boosting based feature selection for lung cancer subtype classification

2017 ◽  
Vol 86 ◽  
pp. 98-106 ◽  
Author(s):  
Juan Ramos-González ◽  
Daniel López-Sánchez ◽  
Jose A. Castellanos-Garzón ◽  
Juan F. de Paz ◽  
Juan M. Corchado
Keyword(s):  
Lung Cancer ◽  
Feature Selection ◽  
Gradient Boosting ◽  
Subtype Classification ◽  
Cancer Subtype ◽  
Selection For

Classification of lung cancer using ensemble-based feature selection and machine learning methods

2015 ◽  
Vol 11 (3) ◽  
pp. 791-800 ◽  
Author(s):  
Zhihua Cai ◽  
Dong Xu ◽  
Qing Zhang ◽  
Jiexia Zhang ◽  
Sai-Ming Ngai ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Feature Selection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Learning Methods ◽  
Machine Learning Methods

The ensemble-based feature selection method presents the merit of acquisition of more informative and compact features than those obtained by individual methods.

Improvement of feature selection method in spam filtering

2009 ◽  
Vol 29 (10) ◽  
pp. 2812-2815
Author(s):  
Yang-zhu LU ◽  
Xin-you ZHANG ◽  
Yu QI
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Spam Filtering
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