Search research articles

Related Concept Videos

Cluster Sampling Method

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Adipose stem cells-derived microvesicles and chicken egg-derived exosomes attenuate cardiac ischemia/reperfusion injury through AKT/ERK/Nrf2/HO-1 axis to inhibit apoptosis and inflammation and restore autophagy.

Life sciences·2026

Same author

Healthcare resource savings from administering paliperidone palmitate once every 3 months instead of once every month for schizophrenia: a 12-month mirror-image analysis of real-world population-based data.

Therapeutic advances in psychopharmacology·2026

Same author

Tackling Imbalanced Data in Chronic Obstructive Pulmonary Disease Diagnosis: An Ensemble Learning Approach with Synthetic Data Generation.

Bioengineering (Basel, Switzerland)·2026

Same author

Therapeutic targeting of the FSP1-ACSL4 axis reverses cisplatin resistance via ferroptosis induction in drug-tolerant papillary thyroid carcinoma.

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery·2026

Same author

RHOJ-induced chemotherapy resistance through epithelial-mesenchymal transition in drug-tolerant persister cells of head and neck cancer.

Translational oncology·2026

Same author

Targeting the DKK1/CSF1 signaling axis to reprogram M2 macrophages and reverse chemoresistance in head and neck squamous cell carcinoma.

European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences·2026

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Jun 27, 2025

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

Utilizing Nearest-Neighbor Clustering for Addressing Imbalanced Datasets in Bioengineering.

Chih-Ming Huang¹, Chun-Hung Lin¹, Chuan-Sheng Hung¹

¹Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung 833, Taiwan.

Bioengineering (Basel, Switzerland)

|April 27, 2024

Summary

This summary is machine-generated.

This study introduces the Location-based Nearest-Neighbor (LBNN) algorithm to improve imbalanced classification. LBNN enhances outlier detection for better performance in tasks like medical diagnosis and fault detection.

Keywords:

K-means with outlier removal (KMOR)Location-based Nearest Neighbor (LBNN)One-Class Nearest-Neighbor (OCNN)

More Related Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Related Experiment Videos

Last Updated: Jun 27, 2025

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Area of Science:

Machine Learning
Data Mining
Pattern Recognition

Background:

Imbalanced classification is prevalent in critical domains like fault diagnosis, intrusion detection, and medical diagnosis.
Acquiring sufficient abnormal data for training is a significant challenge in these fields.
Existing methods often struggle with the inherent data scarcity of abnormal instances.

Purpose of the Study:

To refine the One-Class Nearest-Neighbor (OCNN) algorithm for improved imbalanced classification.
To introduce a novel algorithm, the Location-based Nearest-Neighbor (LBNN), for one-class problems.
To enhance outlier identification and parameter optimization in imbalanced datasets.

Main Methods:

Replaced the inter-quartile range mechanism in OCNN with K-means with outlier removal (KMOR) for robust outlier identification.
Optimized algorithm parameters by treating identified outliers as non-target-class samples.
Developed the LBNN algorithm, which clusters one-class data using KMOR and determines class membership based on farthest distance and percentile calculations for test data.

Main Results:

The LBNN algorithm demonstrated superior performance across various metrics, including precision, recall, and G-means.
Experiments validated the algorithm on eight standard imbalanced datasets from KEEL.
Successful application on three real-world medical imbalanced datasets confirmed its practical effectiveness.

Conclusions:

The refined OCNN approach, particularly the LBNN algorithm, offers a significant advancement in handling imbalanced classification problems.
LBNN provides a robust and effective solution for scenarios where abnormal data is scarce.
The algorithm's performance indicates its potential for widespread application in critical diagnostic and detection systems.