Improved Random Forest (RF) classifier for imbalanced classification of lung nodules

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dc.contributor.authorPaing, May Phu
dc.contributor.authorChoomchuay, Somsak
dc.date.accessioned2026-08-06T10:20:59Z
dc.date.available2026-08-06T10:20:59Z
dc.date.issued2018-08-13
dc.description.abstractComputer-aided detection (CAD) for lung cancer acts a dynamic research in biomedical engineering. These CADs generally occur imbalanced data classification because there are a large number of false lesions which are non-nodules (majority class), compared to the actual nodules (minority class). This paper proposes an improved random forest (RF) classifier to solve the learning bias problem of the imbalanced classification. The proposed RF applies sampling and three feature selection schemes namely Relief, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to improve the classification performance.
dc.identifier.citationIceast 2018 4th International Conference on Engineering Applied Sciences and Technology Exploring Innovative Solutions for Smart Society, 2018
dc.identifier.doi10.1109/ICEAST.2018.8434402
dc.identifier.other2-s2.0-85053106210
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/8866
dc.sourceIceast 2018 4th International Conference on Engineering Applied Sciences and Technology Exploring Innovative Solutions for Smart Society
dc.subjectgenetic algorithm
dc.subjectimbalanced classification
dc.subjectpartical swarm optimization
dc.subjectrandom forest
dc.subjectrelieff
dc.titleImproved Random Forest (RF) classifier for imbalanced classification of lung nodules
dc.typeConference Paper

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