A Rebalancing Framework for Classification of Imbalanced Medical Appointment No-show Data

A Rebalancing Framework for Classification of Imbalanced Medical Appointment No-show Data

This paper aims to improve the classification performance when the data is imbalanced by applying different sampling techniques available in Machine Learning.

Bibliographic Details
Main Authors:	Krishnan Ulagapriya, Sangar Pushpa
Format:	Article
Language:	English
Published:	Sciendo 2021-01-01
Series:	Journal of Data and Information Science
Subjects:	imbalanced data sampling methods machine learning classification
Online Access:	https://doi.org/10.2478/jdis-2021-0011

Similar Items

How Far Have We Progressed in the Sampling Methods for Imbalanced Data Classification? An Empirical Study
by: Zhongbin Sun, et al.
Published: (2023-10-01)

Lightweight Network with Variable Asymmetric Rebalancing Strategy for Small and Imbalanced Fault Diagnosis
by: Biao Chen, et al.
Published: (2022-09-01)

An investigation of machine learning algorithms and data augmentation techniques for diabetes diagnosis using class imbalanced BRFSS dataset
by: Mohammad Mihrab Chowdhury, et al.
Published: (2024-06-01)

A New Multi-Class Rebalancing Framework for Imbalance Medical Data
by: Jafhate Edward, et al.
Published: (2023-01-01)

A multi-manifold learning based instance weighting and under-sampling for imbalanced data classification problems
by: Tayyebe Feizi, et al.
Published: (2023-10-01)

EmSM: Ensemble Mixed Sampling Method for Classifying Imbalanced Intrusion Detection Data
by: Ilok Jung, et al.
Published: (2022-04-01)

Imbalanced Data Classification:A Survey and Experiments in Medical Domain
by: JIANG Hao-chen, WEI Zi-qi, LIU Lin, CHEN Jun
Published: (2022-01-01)

An Improved Extreme Learning Machine for Imbalanced Data Classification
by: Xiaopeng Zhang, et al.
Published: (2022-01-01)

Imbalanced data classification using MapReduce and relief
by: Joanna Jedrzejowicz, et al.
Published: (2018-04-01)

A Submodular Optimization Framework for Imbalanced Text Classification With Data Augmentation
by: Eyor Alemayehu, et al.
Published: (2023-01-01)

Tomek Link and SMOTE Approaches for Machine Fault Classification with an Imbalanced Dataset
by: Elsie Fezeka Swana, et al.
Published: (2022-04-01)

KDE-Based Ensemble Learning for Imbalanced Data
by: Firuz Kamalov, et al.
Published: (2022-08-01)

Two Novel SMOTE Methods for Solving Imbalanced Classification Problems
by: Yuan Bao, et al.
Published: (2023-01-01)

IRS-BAG-Integrated Radius-SMOTE Algorithm with Bagging Ensemble Learning Model for Imbalanced Data Set Classification
by: Lilis Yuningsih, et al.
Published: (2023-10-01)

A Quadratic Surface Minimax Probability Machine for Imbalanced Classification
by: Xin Yan, et al.
Published: (2023-01-01)

Three-Stage Sampling Algorithm for Highly Imbalanced Multi-Classification Time Series Datasets
by: Haoming Wang
Published: (2023-10-01)

An Ensemble Learning Algorithm Based on Density Peaks Clustering and Fitness for Imbalanced Data
by: Hui Xu, et al.
Published: (2022-01-01)

Optimization of data resampling through GA for the classification of imbalanced datasets
by: Filippo Galli, et al.
Published: (2019-10-01)

Data-Centric Optimization Approach for Small, Imbalanced Datasets
by: Vladislav Tanov
Published: (2023-01-01)

Decision analysis framework for predicting no-shows to appointments using machine learning algorithms
by: Carolina Deina, et al.
Published: (2024-01-01)

Feature selection for classification using WGCNA and Spread Sub-Sample for an imbalanced rheumatoid arthritis RNASEQ data
by: Consolata Gakii, et al.
Published: (2023-01-01)

A Hybrid Undersampling-SMOTE Method for Imbalanced Big Data Classification
by: Shaymaa Ahmed Razoqi, et al.
Published: (2023-12-01)

Neuro-evolutionary models for imbalanced classification problems
by: Israa Al-Badarneh, et al.
Published: (2022-06-01)

Uncertainty Based Under-Sampling for Learning Naive Bayes Classifiers Under Imbalanced Data Sets
by: Christos K. Aridas, et al.
Published: (2020-01-01)

Application of Imbalanced Data Classification Quality Metrics as Weighting Methods of the Ensemble Data Stream Classification Algorithms
by: Weronika Wegier, et al.
Published: (2020-07-01)

SMOTE-BD: An Exact and Scalable Oversampling Method for Imbalanced Classification in Big Data
by: María José Basgall, et al.
Published: (2018-12-01)

Cost-Sensitive Variational Autoencoding Classifier for Imbalanced Data Classification
by: Fen Liu, et al.
Published: (2022-04-01)

Statistical Analysis of Imbalanced Classification with Training Size Variation and Subsampling on Datasets of Research Papers in Biomedical Literature
by: Jose Dixon, et al.
Published: (2023-12-01)

Regularized Discrete Optimal Transport for Class-Imbalanced Classifications
by: Jiqiang Chen, et al.
Published: (2024-02-01)

Damage Detection in Structures by Using Imbalanced Classification Algorithms
by: Kasra Yousefi Moghadam, et al.
Published: (2024-01-01)

Exploring the Effect of Balanced and Imbalanced Multi-Class Distribution Data and Sampling Techniques on Fruit-Tree Crop Classification Using Different Machine Learning Classifiers
by: Yingisani Chabalala, et al.
Published: (2023-01-01)

The Imbalanced Classification of Fraudulent Bank Transactions Using Machine Learning
by: Alexey Ruchay, et al.
Published: (2023-06-01)

Difficulty Factors and Preprocessing in Imbalanced Data Sets: An Experimental Study on Artificial Data
by: Wojciechowski Szymon, et al.
Published: (2017-06-01)

Imbalanced biomedical data classification using self-adaptive multilayer ELM combined with dynamic GAN
by: Liyuan Zhang, et al.
Published: (2018-12-01)

Prediction Is a Balancing Act: Importance of Sampling Methods to Balance Sensitivity and Specificity of Predictive Models Based on Imbalanced Chemical Data Sets
by: Priyanka Banerjee, et al.
Published: (2018-08-01)

Learning to Classify DWDM Optical Channels from Tiny and Imbalanced Data
by: Paweł Cichosz, et al.
Published: (2021-11-01)

BUILDING CLASSIFICATION MODELS FROM IMBALANCED FRAUD DETECTION DATA
by: Terence Yong Koon Beh, et al.
Published: (2014-10-01)

A Novel Oversampling Method for Imbalanced Datasets Based on Density Peaks Clustering
by: Jie Cao*, et al.
Published: (2021-01-01)

A weighted pattern matching approach for classification of imbalanced data with a fireworks-based algorithm for feature selection
by: N. K. Sreeja
Published: (2019-04-01)

Imbalanced Data Classification Method Based on LSSASMOTE
by: Zhi Wang, et al.
Published: (2023-01-01)