ArticleMolecules (Basel, Switzerland)2017
Cancer Classification Based on Support Vector Machine Optimized by Particle Swarm Optimization and Artificial Bee Colony.
Article in Molecules (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Smart responsive hydrogels combined with AI design for tumor therapy.Acta pharmaceutica Sinica. B · 2026Review
- An improved multiclass classification of acute lymphocytic leukemia using enhanced glowworm swarm optimization.Scientific reports · 2025Article
- Tuning of controller parameters using Pythagorean fuzzy similarity measure for stable and time delayed unstable plants.PeerJ. Computer science · 2023Article
- Nature-inspired metaheuristics model for gene selection and classification of biomedical microarray data.Medical & biological engineering & computing · 2022Article
- Automated Recognition of Cancer Tissues through Deep Learning Framework from the Photoacoustic Specimen.Contrast media & molecular imaging · 2022Article
- A Novel, Low Computational Complexity, Parallel Swarm Algorithm for Application in Low-Energy Devices.Sensors (Basel, Switzerland) · 2021Article
- A Novel Method for Colorectal Cancer Screening Based on Circulating Tumor Cells and Machine Learning.Entropy (Basel, Switzerland) · 2021Article
- Machine learning modelling of blood lipid biomarkers in familial hypercholesterolaemia versus polygenic/environmental dyslipidaemia.Scientific reports · 2021Article
- Cancer Classification with a Cost-Sensitive Naive Bayes Stacking Ensemble.Computational and mathematical methods in medicine · 2021Article
- A Multiscale Topographical Analysis Based on Morphological Information: The HEVC Multiscale Decomposition.Materials (Basel, Switzerland) · 2020Article
- Comparison between support vector machine and deep learning, machine-learning technologies for detecting epiretinal membrane using 3D-OCT.International ophthalmology · 2019Article
Corrections and comments
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Authors and funding
3 authors.
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Abstract
Intelligent optimization algorithms have advantages in dealing with complex nonlinear problems accompanied by good flexibility and adaptability. In this paper, the FCBF (Fast Correlation-Based Feature selection) method is used to filter irrelevant and redundant features in order to improve the quality of cancer classification. Then, we perform classification based on SVM (Support Vector Machine) optimized by PSO (Particle Swarm Optimization) combined with ABC (Artificial Bee Colony) approaches, which is represented as PA-SVM. The proposed PA-SVM method is applied to nine cancer datasets, including five datasets of outcome prediction and a protein dataset of ovarian cancer. By comparison with other classification methods, the results demonstrate the effectiveness and the robustness of the proposed PA-SVM method in handling various types of data for cancer classification.
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Registered trials
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