ArticleBMC bioinformatics2013
Improved shrunken centroid classifiers for high-dimensional class-imbalanced data.
Article in BMC bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed, 51 citations in OpenAlex.
- Development of an Explainable Machine Learning Model to Predict Mortality Risk in Sepsis Patients: Insights From a Real-World Clinical Data.Shock (Augusta, Ga.) · 2026Article
- [Early warning model of postoperative infection of internal fixation device in maxillofacial fracture based on the synthetic minority over-sampling technique algorithm].Hua xi kou qiang yi xue za zhi = Huaxi kouqiang yixue zazhi = West China journal of stomatology · 2025Article
- Association of Premature Ventricular Contraction (PVC) with hematological parameters: a data mining approach.Scientific reports · 2025Article
- Deep centroid: a general deep cascade classifier for biomedical omics data classification.Bioinformatics (Oxford, England) · 2024Article
- Association between biochemical and hematologic factors with COVID-19 using data mining methods.BMC infectious diseases · 2023Article
- Use of data mining approaches to explore the association between type 2 diabetes mellitus with SARS-CoV-2.BMC pulmonary medicine · 2023Article
- Prediction of type 2 diabetes mellitus using hematological factors based on machine learning approaches: a cohort study analysis.Scientific reports · 2023Article
- Multi-level predictors of depression symptoms in the Adolescent Brain Cognitive Development (ABCD) study.Journal of child psychology and psychiatry, and allied disciplines · 2022Article
- Development of a Machine Learning-Based Screening Method for Thyroid Nodules Classification by Solving the Imbalance Challenge in Thyroid Nodules Data.Journal of research in health sciences · 2022Article
- Comparison of mortality prediction models for road traffic accidents: an ensemble technique for imbalanced data.BMC public health · 2022Article
- Cycle-consistent adversarial networks improves generalizability of radiomics model in grading meningiomas on external validation.Scientific reports · 2022Article
- Establishment of a prediction tool for ocular trauma patients with machine learning algorithm.International journal of ophthalmology · 2021Article
- A deep learning approach to predict blood-brain barrier permeability.PeerJ. Computer science · 2021Article
- Prostate cancer in young men represents a distinct clinical phenotype: gene expression signature to predict early metastases.Journal of translational genetics and genomics · 2021Article
- Predictive Models of Fever, ICU Transfer, and Mortality in Hospitalized Patients With Neutropenia.Critical care explorations · 2020Article
- Prediction of perinatal death using machine learning models: a birth registry-based cohort study in northern Tanzania.BMJ open · 2020Article
- Article
- Molecular subtypes of triple-negative breast cancer in women of different race and ethnicity.Oncotarget · 2019Article
- Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPAM, a nearest shrunken centroid method (NSC), is a popular classification method for high-dimensional data. ALP and AHP are NSC algorithms that were proposed to improve upon PAM. The NSC methods base their classification rules on shrunken centroids; in practice the amount of shrinkage is estimated minimizing the overall cross-validated (CV) error rate.
resultsWe show that when data are class-imbalanced the three NSC classifiers are biased towards the majority class. The bias is larger when the number of variables or class-imbalance is larger and/or the differences between classes are smaller. To diminish the class-imbalance problem of the NSC classifiers we propose to estimate the amount of shrinkage by maximizing the CV geometric mean of the class-specific predictive accuracies (g-means).
conclusionsThe results obtained on simulated and real high-dimensional class-imbalanced data show that our approach outperforms the currently used strategy based on the minimization of the overall error rate when NSC classifiers are biased towards the majority class. The number of variables included in the NSC classifiers when using our approach is much smaller than with the original approach. This result is supported by experiments on simulated and real high-dimensional class-imbalanced data.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.