A support vector machine-based ensemble algorithm for breast cancer diagnosis

DOI10.1016/j.ejor.2017.12.001zbMath1403.92109OpenAlexW2773381949MaRDI QIDQ723956

Sang Won Yoon, Bichen Zheng, Hoo Sang Ko, Hai-Feng Wang

Publication date: 25 July 2018

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejor.2017.12.001

zbMATH Keywords

variance reductionsupport vector machineensemble learninganalyticscancer diagnoses

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Learning and adaptive systems in artificial intelligence (68T05) Medical applications (general) (92C50)

Related Items (9)

Breast cancer diagnosis using feature extraction and boosted C5.0 decision tree algorithm with penalty factor ⋮ Tightening big Ms in integer programming formulations for support vector machines with ramp loss ⋮ Data-driven decision model based on local two-stage weighted ensemble learning ⋮ Functional-bandwidth kernel for support vector machine with functional data: an alternating optimization algorithm ⋮ Feature selection and tumor classification for microarray data using relaxed Lasso and generalized multi-class support vector machine ⋮ Kernel-based online regression with canal loss ⋮ A maximum-margin multisphere approach for binary multiple instance learning ⋮ Unsupervised quadratic surface support vector machine with application to credit risk assessment ⋮ A hybrid two-stage squeezenet and support vector machine system for Parkinson's disease detection based on handwritten spiral patterns

Uses Software

• AdaBoost.MH

Cites Work

• Bagging predictors
• Ensemble strategies for a medical diagnostic decision support system: A breast cancer diagnosis application
• A bias/variance decomposition for models using collective inference
• Fuzzy rule based classification with FeatureSelector and modified threshold accepting
• Support-vector networks
• Supervised fuzzy clustering for the identification of fuzzy classifiers
• Breast Cancer Diagnosis and Prognosis Via Linear Programming
• A tutorial on ν‐support vector machines
• Pattern classification with principal component analysis and fuzzy rule bases
• Choosing multiple parameters for support vector machines
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