Results 1 to 10 of about 179,516 (314)

Transformers deep learning models for missing data imputation: an application of the ReMasker model on a psychometric scale [PDF]

Frontiers in Psychology
IntroductionMissing data in psychometric research presents a substantial challenge, impacting the reliability and validity of study outcomes. Various factors contribute to this issue, including participant non-response, dropout, or technical errors ...
Monica Casella, Nicola Milano, Pasquale Dolce, Davide Marocco   +3 more
doaj   +2 more sources

Transmogrified Imputation Algorithm for Clustering Data in Missing Data Imputation

International Journal for Research in Applied Science and Engineering Technology, 2019
A. Linda Sherin
openalex   +2 more sources

One Model to Synthesize Them All: Multi-Contrast Multi-Scale Transformer for Missing Data Imputation. [PDF]

IEEE Trans Med Imaging, 2023
Liu J, Pasumarthi S, Duffy B, Gong E, Datta K, Zaharchuk G.   +5 more
europepmc   +3 more sources

A comparison of imputation methods for categorical data

Informatics in Medicine Unlocked, 2023
Objectives: Missing data is commonplace in clinical databases, which are being increasingly used for research. Without giving any regard to missing data, results from analysis may become biased and unrepresentative.
Shaheen MZ. Memon, Robert Wamala, Ignace H. Kabano   +2 more
doaj   +1 more source

Multiple Data Imputation Methods Advance Risk Analysis and Treatability of Co-occurring Inorganic Chemicals in Groundwater. [PDF]

Environ Sci Technol
Mahmood AU, Islam M, Gulyuk AV, Briese E, Velasco CA, Malu M, Sharma N, Spanias A, Yingling YG, Westerhoff P.   +9 more
europepmc   +2 more sources

SICE: an improved missing data imputation technique

Journal of Big Data, 2020
In data analytics, missing data is a factor that degrades performance. Incorrect imputation of missing values could lead to a wrong prediction. In this era of big data, when a massive volume of data is generated in every second, and utilization of these ...
Shahidul Islam Khan, Abu Sayed Md Latiful Hoque   +1 more
doaj   +1 more source

A Systematic Literature Review On Missing Values: Research Trends, Datasets, Methods and Frameworks [PDF]

E3S Web of Conferences, 2023
Handling of missing values in data analysis is the focus of attention in various research fields. Imputation is one method that is commonly used to overcome this problem of missing data.
Setiawan Ismail, Gernowo Rahmat, Warsito Budi   +2 more
doaj   +1 more source

Missing Value Imputation Using Contemporary Computer Capabilities: An Application to Financial Statements Data in Large Panels [PDF]

Economic and Business Review, 2017
This paper addresses an evaluation of the methods for automatic item imputation to large datasets with missing data in the particular setting of financial data often used in economic and business settings.
Ales Gorisek, Marko Pahor
doaj   +1 more source

Informer-WGAN: High Missing Rate Time Series Imputation Based on Adversarial Training and a Self-Attention Mechanism

Algorithms, 2022
Missing observations in time series will distort the data characteristics, change the dataset expectations, high-order distances, and other statistics, and increase the difficulty of data analysis.
Yufan Qian, Limei Tian, Baichen Zhai, Shufan Zhang, Rui Wu   +4 more
doaj   +1 more source

Advanced methods for missing values imputation based on similarity learning [PDF]

PeerJ Computer Science, 2021
The real-world data analysis and processing using data mining techniques often are facing observations that contain missing values. The main challenge of mining datasets is the existence of missing values.
Khaled M. Fouad, Mahmoud M. Ismail, Ahmad Taher Azar, Mona M. Arafa   +3 more
doaj   +2 more sources