Can-CSC-GBE: Developing Cost-sensitive Classifier with Gentleboost Ensemble for breast cancer classification using protein amino acids and imbalanced data-Reference-Cited by-同舟云学术

Can-CSC-GBE: Developing Cost-sensitive Classifier with Gentleboost Ensemble for breast cancer classification using protein amino acids and imbalanced data

Published:2016-06 Issue: Volume:73 Page:38-46
ISSN:0010-4825
Container-title:Computers in Biology and Medicine
language:en
Short-container-title:Computers in Biology and Medicine

Author:

Ali Safdar,Majid Abdul,Javed Syed Gibran,Sattar Mohsin

Funder

Higher Education Commission, Government of Pakistan

Publisher

Elsevier BV

Subject

Health Informatics,Computer Science Applications

Reference28 articles.

1. IARC GLOBOCAN 2012, Estimated cancer incidence, mortality and prevalence worldwide, 2012.

2. A new feature extraction framework based on wavelets for breast cancer diagnosis;Ergin;Comput. Biol. Med.,2015

3. Breast cancer detection from MR images through an auto-probing discrete Fourier transform system;Sim;Comput. Biol. Med.,2015

4. Computer-aided diagnosis system based on fuzzy logic for breast cancer categorization;Miranda;Comput. Biol. Med.,2014

5. IDM-PhyChm-Ens: intelligent decision-making ensemble methodology for classification of human breast cancer using physicochemical properties of amino acids;Safdar;Amino Acids,2014

Cited by 33 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Predicting the Performance of Ensemble Classification Using Conditional Joint Probability;Mathematics;2024-08-21

2. Enhancing sensitivity in lung nodule malignancy classification: incorporating cost values into deep learning-based CAD systems;Medical Imaging 2024: Computer-Aided Diagnosis;2024-04-03

3. Iterative Tuning of Tree-Ensemble-Based Models' parameters Using Bayesian Optimization for Breast Cancer Prediction;Informatics and Automation;2024-01-11

4. Class imbalanced problem: Taxonomy, open challenges, applications and state-of-the-art solutions;China Communications;2024

5. Learning from Imbalanced Data in Healthcare: State-of-the-Art and Research Challenges;Studies in Computational Intelligence;2024