Synthetic sampling from small datasets: A modified mega-trend diffusion approach using k-nearest neighbors-Reference-Cited by-同舟云学术

Synthetic sampling from small datasets: A modified mega-trend diffusion approach using k-nearest neighbors

Published:2022-01 Issue: Volume:236 Page:107687
ISSN:0950-7051
Container-title:Knowledge-Based Systems
language:en
Short-container-title:Knowledge-Based Systems

Author:

Sivakumar Jayanth^ORCID,Ramamurthy Karthik,Radhakrishnan Menaka,Won Daehan^ORCID

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Management Information Systems,Software

Reference60 articles.

1. Using mega-trend-diffusion and artificial samples in small data set learning for early flexible manufacturing system scheduling knowledge;Li;Comput. Oper. Res.,2007

2. Generation and evaluation of synthetic patient data;Goncalves;BMC Med. Res. Methodol.,2020

3. Novel Early-Warning System for Evaluating Control and Elimination of SARSCoV-2 Reveals Alignment of Policy with Local Transmission in New Zealand;Parag,2020

4. Statistical disclosure limitation;Rubin;J. Off. Stat.,1993

5. Synthetic Datasets for Statistical Disclosure Control: Theory and Implementation, Vol. 201;Drechsler,2011

Cited by 14 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. NMNN: Newtonian Mechanics-based Natural Neighbor algorithm;Information Sciences;2024-10

2. Forecasting Population Migration in Small Settlements Using Generative Models under Conditions of Data Scarcity;Smart Cities;2024-09-03

3. Estimation of Peanut Southern Blight Severity in Hyperspectral Data Using the Synthetic Minority Oversampling Technique and Fractional-Order Differentiation;Agriculture;2024-03-15

4. Applying data augmentation technique on blast-induced overbreak prediction: Resolving the problem of data shortage and data imbalance;Expert Systems with Applications;2024-03

5. ARKA: a framework of dimensionality reduction for machine-learning classification modeling, risk assessment, and data gap-filling of sparse environmental toxicity data;Environmental Science: Processes & Impacts;2024